Hire Expert Data Scientists

Hire Top 1% Data Scientists. No Freelancers. Pre-vetted Experts

Enhance your team's capabilities with our expert data scientists, possessing an average of 5 years of hands-on experience in crafting sophisticated, data-driven solutions, including efficient handling of vast amounts of unstructured data.

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  • Flexible contracts, transparent pricing
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Services Offered by Our Data Scientists

Uncover the full spectrum of services provided by our adept data scientists, designed to transform raw data into actionable insights that drive meaningful outcomes.

Data Gathering and Preprocessing

Our data scientists excel in collecting structured and unstructured data through web scraping and API integration. They then apply advanced techniques like feature engineering and data normalization to ensure the data is meticulously prepared for model training.

Data Annotation

Using a combination of manual techniques and cutting-edge tools like Hugging Face's datasets library, our data scientists meticulously label and categorize data. This enables machine learning algorithms to recognize patterns and make highly accurate predictions.

Algorithm Selection and Hyperparameter Tuning

With expertise in exploratory data analysis (EDA) and hypothesis testing, our data scientists choose the most suitable ML algorithm for your project. They employ methods like Grid search and Bayesian optimization to fine-tune hyperparameters, ensuring optimal model efficiency.

Model Training and Validation

Our data scientists employ various ML techniques, including supervised, unsupervised, and reinforcement learning, to train the model effectively. They validate the model using cross-validation, confusion matrix, and ROC curve analysis to guarantee accuracy.

Model Evaluation

Post-deployment, our data scientists for hire meticulously assess the model's performance using evaluation metrics such as precision, accuracy, recall, and F1 score. Any anomalies are promptly resolved, and underperforming segments are addressed to enhance overall performance.


Our data scientists for hire thoroughly analyze your business requirements and data. They unearth valuable insights and develop a comprehensive data-driven strategy, empowering you to make informed decisions and drive business growth effectively.

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Methods We Use to Extract Data Insights

Here is the comprehensive list of techniques we employ, harnessing the power of data to unveil invaluable insights that pave the way for informed decisions and innovative solutions.


Machine Learning Algorithms

Our data scientists employ various ML algorithms, including decision trees, linear regression, logistic regression, random forests, support vector machines, and KNN. These algorithms are used for classification, regression, clustering, and dimensionality reduction to build robust AI models.

Deep Learning

Utilizing cutting-edge deep learning algorithms such as neural networks, CNNs, RNNs, and autoencoders, our data scientists derive valuable insights from datasets and construct accurate AI models for diverse use cases.

Supervised Learning

Our data scientists for hire carefully select and curate labeled data to train AI models effectively. They choose appropriate model architectures, define loss function optimization algorithms, and fine-tune hyperparameters for optimal performance.

Unsupervised Learning

Our data scientists leverage unsupervised learning algorithms to discover patterns and relationships in handling unlabeled data. They conduct insightful assessments and interpretations to draw meaningful conclusions.

Transfer Learning

We choose pre-trained models relevant to the task at hand to expedite model training. Our data scientists curate the dataset for fine-tuning the model and optimizing hyperparameters to achieve peak performance.

Reinforcement Learning

Using developer resources and advanced tools like Markov Decision Processes, our data scientists apply reinforcement learning techniques. This enables them to train agents to maximize rewards based on environmental feedback while performing tasks.

NLP (Natural Language Processing)

Our data scientists utilize NLP toolkits such as NLTK and SpaCy to unlock insights from text data. They implement tokenization, stemming, and lemmatization to identify root words and simplify the data into smaller components.

Technical Expertise of Our Data Scientists

Our data scientists stay updated about the latest trends and technologies. Here are some of the tech stacks they can work on:


AI Frameworks & Programming Languages

Integration and Deployment Tools

  • docker
  • Kubernetes

Databases & Cloud Platforms

Data Pre-processing & Visualization Tools

  • Numpy
  • Plotly
  • ML
  • Scikit learn

Algorithms & Collaborative Tools

  • Supervised/Unsupervised Learning
  • Clustering
  • Metric Learning
  • Fewshot Learning
  • Ensemble Learning
  • Online Learning
  • Github
  • Jupyter

Neural Networks

  • CNN
  • RNN
  • Representation Learning
  • Manifold Learning
  • Variational Autoencoders
  • Bayesian Netwrok
  • Autoregressive Networks
  • Long Short-term Memory

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How We Ensure Code Quality

At the core of our development process, we prioritize code quality, implementing stringent testing, detailed reviews, and industry-best practices to deliver software that excels in both functionality and longevity.


Upholding Coding Best Practices

  • Using descriptive variable names and in-code comments for better readability and maintainability.
  • Comprehensive documentation for every codebase, ensuring clarity and ease of future updates.

Unit Testing

  • Integrating continuous integration tools to automatically run unit tests on new code submissions, ensuring immediate feedback on code integrity.
  • Utilizing test-driven development (TDD) practices to encourage the creation of tests before writing code, leading to more robust and error-free components.

Code Review Practices

  • Incorporating automated code scanning tools to detect vulnerabilities and code smells before manual review, streamlining the review process.
  • Establishing a peer review culture where developers are encouraged to provide constructive feedback, promoting knowledge sharing and collaborative improvement.

Code Quality Metrics

  • Adopting complexity metrics to identify overly complex code that may be harder to maintain and test, aiming for simplicity and readability.
  • Tracking technical debt metrics to quantify the cost of rework associated with quick fixes versus proper solutions, guiding towards long-term code health.

We are India’s Top

Company to Hire Data Scientists

From Startups to Enterprises, Product Companies to Government Entities, and SMEs to Digital Agencies, we cater to diverse technology needs across the board. Hire offshore data scientists for their relentless commitment.

We enable businesses worldwide to scale, transform, and gain a competitive edge through our expertly delivered innovative offshore programming services. Our industry recognition stems from the following unique selling points (USPs):

  • WorkstatusTM powered Proof of Work
  • Top 1% developers, rigorously vetted
  • Dedicated project manager
  • Flexible contracts, transparent pricing
  • Zero hiring fee, quick onboarding
  • Comprehensive code documentation
  • Adherence to data security practices
  • Language/time-zone compatible staff
  • Valuecoders
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Awards & Certifications -

From startups to big enterprises, development


From startups to big enterprises, development


From startups to big enterprises, development


From startups to big enterprises, development


From startups to big enterprises, development


From startups to big enterprises, development


From startups to big enterprises, development


From startups to big enterprises, development

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Ready to elevate your software projects? Hire our expert developers and experience unparalleled innovation and efficiency.

675+ Full-time Staff projects executed successfully
19+ Years Experience Years Of Experience in this feild
25,000+ Satisfied
Total No. of Satisfied Customers

Comparative Analysis: In-House, Freelancers Or ValueCoders

If you wish to hire HTML5 developers, you can hire an in-house team, freelance team, or partner with a trusted software development company like ValueCoders. Here is a comparative analysis of three models.

  • Factor
  • Time to get right developers
  • Time to start a project
  • Recurring cost of training & benefits
  • Time to scale size of the team
  • Pricing (weekly average)
  • Project failure risk
  • Developers backed by delivery team
  • Dedicated resources
  • Quality guarantee
  • Tools and professional enviroment
  • Agile development methodology
  • Impact due to turnover
  • Structured training programs
  • Communications
  • Termination costs
  • Assured work rigor
  • ValueCoders
  • 1 day - 2 weeks
  • 1 day - 2 weeks
  • 0
  • 48 hours - 1 week
  • 1.5X
  • Extremely low, we have a 98% success ratio
  • Yes
  • Yes
  • High
  • Yes
  • Yes
  • None
  • Yes
  • Seamless
  • None
  • 40hrs / week
  • In-house
  • 4 - 12 weeks
  • 2 - 10 weeks
  • $10,000 -$30,000
  • 4 - 16 weeks
  • 2X
  • Low
  • Some
  • Yes
  • High
  • Yes
  • Some
  • High
  • Some
  • Seamless
  • High
  • 40hrs / week
  • Freelancer
  • 1 - 12 weeks
  • 1 - 10 weeks
  • 0
  • 1 - 12 weeks
  • 1X
  • Very High
  • No
  • Some
  • Uncertain
  • Uncertain
  • No
  • High
  • No
  • Uncertain
  • None
  • 40hrs / week

How We Hire Developers?

With a five-step hiring process in place, we are committed to onboarding
exceptionally productive engineers.


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Take a look at the simple and straightforward process to hire software developers from ValueCoders.



We assess project alignment for potential collaboration.


Developer Selection

We select developers from our tech pool as per project needs.



Upon ETA approval, developers start with direct task assignment.



Modify team size as needed, aided by an account manager.

Collaborate With The Top 1% Data Scientists in India

Hire a team of HTML developers from ValueCoders, to have top-notch development and excellent customer service.

Junior Hire Expert Data Scientists

$1500 - $2200

1-3 Years Experienced

Mid Level Hire Expert Data Scientists

$2200 - $2800

3-5 Years Experienced

Senior Level Hire Expert Data Scientists

$2800 onwards

5+ Years Experienced

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User Guide to Understanding Hiring of Data Scientists

Typical Responsibilities of a Data Scientist

  • Data Collection and Preprocessing: Data scientists gather and clean data from various sources.
  • Data Exploration and Visualization: They explore datasets to identify patterns, trends, and anomalies. Visualization techniques present complex data clearly and understandably.
  • Model Development: Data scientists design and develop machine learning models to solve specific business problems, such as predictive analytics, recommendation systems, or fraud detection.
  • Model Training and Testing: They train machine learning models on historical data and evaluate their performance using validation techniques to ensure they provide accurate predictions.
  • Model Deployment and Integration: Data scientists collaborate with engineers to deploy models into production environments, making them accessible for real-time use.
  • Continuous Monitoring and Maintenance: They continuously monitor model performance and update them.
  • Data Security and Ethics: Data scientists uphold data privacy and security standards, adhering to ethical guidelines in handling sensitive information.
  • Collaboration and Communication: They collaborate with cross-functional teams, including business stakeholders, to understand requirements and communicate insights effectively.

Data Scientists’ Contribution to Business Growth

  • Informed Decision-Making: Data scientists analyze data to provide valuable insights that support data-driven decision-making.
  • Enhanced Efficiency and Productivity: Data scientists help businesses operate more efficiently and boost productivity by automating processes and optimizing workflows.
  • Customer Understanding and Personalization: They analyze customer behavior and preferences, enabling businesses to deliver personalized experiences.
  • Risk Management: Data scientists assist in identifying potential risks and fraud through advanced analytics, helping businesses mitigate losses.
  • Innovation and Competitive Advantage: Data scientists drive innovation and help businesses gain a competitive edge by staying ahead of market trends.
  • Revenue Generation: Data scientists develop predictive models forecasting customer demands, enabling businesses to tailor products and services.
  • Resource Optimization: Data scientists help businesses optimize resource allocation by analyzing data, leading to cost savings and improved operational efficiency.
  • Hire remote data scientists who leverage data to extract actionable insights, leading to business growth, improved decision-making, and a competitive advantage in the market.

Qualifications and Skills of a Good Data Scientist

When hiring a data scientist, consider looking for the following qualifications and skills:

  • Education: A solid educational background in fields like computer science, data science, statistics, mathematics, or related disciplines is essential.
  • Programming Proficiency: Expertise in programming languages like Python or R, as well as knowledge of SQL for data querying and manipulation.
  • Statistical Knowledge: Solid understanding of statistical concepts and methodologies, such as hypothesis testing, regression analysis, and probability.
  • Machine Learning: Experience applying machine learning techniques, including supervised and unsupervised learning, and knowledge of popular libraries like scikit-learn or TensorFlow.
  • Data Visualization: Proficiency in data visualization tools like Matplotlib, Seaborn, or Tableau to present insights visually compelling and easily understandable.
  • Data Manipulation: Skills in handling and preprocessing large datasets using tools like Pandas or NumPy.
  • Problem-Solving: Strong analytical and problem-solving skills to tackle complex business challenges and devise innovative data-driven solutions.
  • Domain Expertise: Familiarity with the specific domain or industry relevant to your business can be valuable, as it helps data scientists better understand the context and requirements.
  • Communication: Effective communication skills are crucial, as data scientists must be able to present their findings and insights to both technical and non-technical stakeholders.
  • Curiosity and Continuous Learning: A genuine curiosity to explore data and a willingness to stay updated with the latest advancements in data science and machine learning.
  • Business Acumen: The ability to understand the business goals and align data science efforts to drive positive outcomes for the organization.
  • Team Player: Collaboration is essential, so a data scientist should be able to work well in cross-functional teams with engineers, analysts, and business stakeholders.
  • Experimentation and Iteration: A data scientist should be comfortable experimenting with different models, iterating, and optimizing to achieve the best results.
  • Ethics and Data Privacy: Awareness of ethical considerations regarding data privacy, confidentiality, and responsible data usage.
  • Portfolio and Projects: Reviewing their portfolio, past projects, or Kaggle competitions can provide insight into their practical skills and problem-solving approach.

You can find a data scientist who will effectively contribute to your organization’s data-driven success by assessing candidates based on these qualifications and skills.

Data Scientists Vs. Data Analysts Vs. Data Engineers

Data Scientists, Data Analysts, and Data Engineers are distinct roles within the data-related domain, each with its focus and responsibilities:

Data Scientists:
Data scientists primarily focus on extracting valuable insights and knowledge from data. They possess a strong background in statistics, mathematics, and machine learning. Their main tasks include data exploration, predictive modeling, building machine learning algorithms, and deriving actionable insights from data to solve complex business problems. They are skilled in developing and training AI models to make data-driven predictions and decisions.

Data Analysts:
Data analysts are responsible for interpreting and analyzing data to support business decision-making. They understand data visualization and reporting tools well and are proficient in SQL and data querying. Their primary tasks involve data cleaning, aggregation, and generating reports and dashboards to present key performance indicators and trends. Data analysts focus on concluding data to facilitate operational and strategic decision-making.

Data Engineers:
Data engineers design and construct the infrastructure and architecture required to manage and process large volumes of data. They are skilled in database management, data warehousing, and ETL (Extract, Transform, Load) processes. Data engineers build and maintain data pipelines that collect, transform, and store data in a format suitable for data scientists and analysts’ analysis. Their role is essential in ensuring data accessibility, reliability, and scalability.

Data scientists utilize advanced statistical and machine-learning techniques to derive insights and build predictive models. Data analysts concentrate on interpreting data and providing reports to aid decision-making. Data engineers create and maintain the data storage and processing infrastructure.

While these roles may have overlapping skills, they each play a unique and complementary part in the data lifecycle, ultimately contributing to the effective use of data in an organization.

Key Qualities for an Exceptional Data Scientist

Beyond technical skills, exceptional data scientists possess key qualities and traits that set them apart in the field:

  • Curiosity: Exceptional data scientists have an innate curiosity to explore data and uncover hidden patterns and insights. They are naturally inquisitive and always eager to ask questions and seek answers.
  • Problem-Solving Abilities: Data scientists excel at solving complex problems using data-driven approaches. They can break down large problems into smaller, manageable tasks and develop innovative solutions.
  • Critical Thinking: They possess strong critical thinking skills, enabling them to evaluate data objectively, identify potential biases, and make informed decisions based on evidence.
  • Adaptability: Data science is an ever-evolving field; exceptional data scientists are adaptable and quick to learn new techniques and tools. They stay updated with the latest advancements to stay relevant in the industry.
  • Communication: Effective communication is vital for data scientists to clearly convey their findings to non-technical stakeholders. Exceptional data scientists can communicate complex concepts simply and understandably.
  • Business Acumen: They understand the broader business context and align data science efforts with the organization’s goals. They recognize how data insights can impact business decisions and contribute to overall growth.
  • Creativity: Exceptional data scientists think creatively when approaching problems and are not limited to conventional solutions. They are open to experimenting with different methodologies to achieve the best outcomes.
  • Collaboration: Data science projects often involve cross-functional teams, and exceptional data scientists work well in collaborative environments. They can effectively collaborate with engineers, analysts, and business stakeholders.
  • Ethical Mindset: Data scientists handle sensitive and private information, and exceptional data scientists prioritize ethical considerations for data privacy, security, and responsible data usage.
  • Persistence: Extracting valuable insights from data can be challenging, and exceptional data scientists persist in exploring various angles and techniques until they find meaningful results.
  • Domain Knowledge: Beyond technical expertise, they acquire domain-specific knowledge that helps them understand the unique challenges and requirements of the industry they are working in.
  • Attention to Detail: They pay meticulous attention to data quality, ensuring that data is accurate, reliable, and suitable for analysis.
  • Time Management: Exceptional data scientists effectively manage their time to meet project deadlines and prioritize tasks efficiently.
  • Empathy: Understanding the needs and perspectives of stakeholders helps data scientists tailor their analyses and recommendations to be more relevant and impactful.
  • Continual Learning: They strongly desire continuous learning and seek opportunities to expand their knowledge and skill set within and beyond data science.

In summary, exceptional data scientists combine technical expertise with these essential qualities and traits to excel in their roles and drive valuable insights and solutions for businesses and organizations.

Ongoing Professional Development Opportunities for Data Scientists

To keep data scientists’ skills sharp and up-to-date, companies should provide the following ongoing professional development opportunities:

  • Training Programs: Companies can organize regular training programs, workshops, and webinars focused on the latest advancements in data science, machine learning, and related technologies.
  • Access to Online Courses: Offering access to online learning platforms like Coursera, Udacity, or DataCamp allows data scientists to explore specialized courses and certifications to enhance their knowledge.
  • Data Science Conferences: Encourage data scientists to attend industry conferences and seminars to learn from experts, network, and gain insights into emerging trends.
  • Hackathons and Competitions: Organizing internal or external data science hackathons and competitions fosters healthy competition and encourages data scientists to tackle real-world challenges.
  • Research and Publication Support: Support data scientists in conducting research and publishing their findings in industry journals or conferences to share knowledge and gain recognition.
  • Mentorship Programs: Pairing experienced data scientists with junior colleagues creates a supportive learning environment and promotes knowledge sharing.
  • Data Science Communities: Encourage participation in data science communities and forums where data scientists can exchange ideas, share experiences, and learn from peers.
  • Book Clubs: Organizing book clubs focused on data science literature enables teams to read and discuss the latest books and research papers.
  • Cross-Functional Projects: Involve data scientists in cross-functional projects with other teams (e.g., marketing, product development) to gain exposure to different domains and challenges.
  • Data Engineering Collaboration: Provide opportunities for data scientists to collaborate with data engineers to understand the data infrastructure and improve their understanding of data pipelines.
  • Access to Research Papers and Journals: Companies can subscribe to relevant research journals and publications, giving data scientists access to the latest research papers and findings.
  • Industry Certifications: Support data scientists in obtaining industry-recognized certifications to validate their skills and knowledge in specific domains or technologies.
  • Time for Self-Study: Allow data scientists to dedicate some time during work hours to study and explore new tools and technologies.
  • Internal Knowledge-Sharing Sessions: Organize regular internal knowledge-sharing sessions where data scientists can present their work, share insights, and receive peer feedback.
  • Performance Reviews with Learning Goals: Incorporate learning and skill development goals into data scientists’ performance evaluations, encouraging a growth mindset.

By providing these ongoing professional development opportunities, companies can ensure that their data scientists stay abreast of the latest advancements, remain motivated, and contribute effectively to the organization’s success.

Potential Challenges Companies Face When Integrating Data Scientists into Existing Teams

Integrating data scientists into existing teams can present several challenges. Here are some potential challenges and strategies to address them:

Communication and Collaboration:
Challenge: Data scientists may have specialized technical knowledge that other team members might not fully understand, leading to communication gaps and challenges in collaboration.

Solution: Encourage regular meetings and open communication channels to foster understanding between data scientists and other team members. Facilitate knowledge-sharing sessions to bridge the gap and encourage cross-functional collaboration.

Aligning Objectives:
Challenge: Data scientists’ objectives might not align with the broader goals of the existing team, leading to conflicting priorities.
Solution: Clearly communicate the team’s overall objectives and how data scientists’ contributions align with them. Involve data scientists in setting team goals to ensure alignment and a shared sense of purpose.

Data Access and Infrastructure:
Challenge: Data scientists may face difficulties in accessing the necessary data or working with outdated infrastructure, hindering their productivity.

Solution: Ensure data scientists have access to relevant data and resources. Collaborate with data engineering teams to improve data pipelines and infrastructure to support data scientists’ needs.

Resistance to Change:
Challenge: Existing team members may be resistant to adopting new data-driven approaches or may feel threatened by the introduction of data scientists.

Solution: Provide training and workshops for the entire team to familiarize them with data science concepts and the benefits of data-driven decision-making. Emphasize the collaborative nature of data science integration and celebrate successful joint projects.

Unrealistic Expectations:
Challenge: Unrealistic expectations about the immediate impact of data science projects can lead to disappointment and frustration.

Solution: Set realistic expectations and timelines for data science initiatives. Educate stakeholders about the iterative nature of data science projects and the time required to produce meaningful results.

Data Privacy and Ethics:
Challenge: Data scientists must adhere to ethical considerations and data privacy regulations, which may require adjustments to existing data practices.

Solution: Provide data scientists with clear guidelines on data privacy and ethics. Involve legal and compliance teams to ensure data practices align with relevant regulations.

Talent Retention:
Challenge: Attracting and retaining skilled data scientists can be challenging due to high demand in the job market.

Solution: Offer competitive compensation packages, opportunities for professional growth, and a supportive work environment to attract and retain top data science talent. Implement mentorship programs to provide career development and growth opportunities.

Measuring Success:
Challenge: Measuring the impact and success of data science initiatives may be challenging, especially in the early stages.

Solution: Establish key performance indicators (KPIs) and success metrics for data science projects. Regularly evaluate and communicate the progress and impact of data science initiatives to stakeholders.

By addressing these challenges proactively and fostering a culture of collaboration and data-driven decision-making, companies can successfully integrate data scientists into existing teams and harness their expertise for organizational growth and success.


Q. Why choose ValueCoders for hiring expert data scientists?

Ans. You should choose ValueCoders to hire expert data scientists for the following reasons:

  • Extensive Expertise: We have a team of highly skilled and experienced data scientists.
  • Tailored Solutions: We offer customized data science services to cater to specific business needs.
  • Proven Track Record: We have a strong track record of successfully delivering data science projects.
  • Cutting-edge Technology: Our data scientists stay updated with the latest tools and technologies.
  • Seamless Integration: We can integrate data scientists into your existing teams, ensuring collaboration and smooth project execution.
  • Data Security: We prioritize data privacy and implement strict security measures.

Q. What industries have your data scientists successfully transformed with their expertise?

Ans.ValueCoders’ data scientists have successfully transformed industries across the board, including:

  • Healthcare and Life Sciences
  • Finance and Banking
  • E-commerce and Retail
  • Manufacturing and Logistics
  • Travel and Hospitality
  • Media and Entertainment
  • Education and E-learning
  • Real Estate and Property
  • Automotive and Transportation

Q. How do your data scientists ensure data privacy and security in their projects?

Ans.ValueCoders’ data scientists ensure data privacy and security through:

  • Strict adherence to data privacy regulations
  • Implementation of robust encryption and access controls
  • Secure data handling and storage practices
  • Regular security audits and risk assessments
  • Confidentiality agreements and non-disclosure policies

Q. How does your data scientists turn raw data into actionable insights?

Ans. Our data scientists turn raw data into actionable insights through:

  • Data cleaning and preprocessing
  • Data exploration to identify patterns and trends
  • Statistical analysis and machine learning techniques

What Our Clients Have to Say About Us

We are grateful for our clients’ trust in us, and we take great pride in delivering quality solutions that exceed their expectations. Here is what some of them have to say about us:

The Project managers took a lot of time to understand our project before coming up with a contract or what they thought we needed. I had the reassurance from the start that the project managers knew what type of project I wanted and what my needs were. That is reassuring, and that's why we chose ValueCoders.

James Kelly
Co-founder, Miracle Choice

The team at ValueCoder has provided us with exceptional services in creating this one-of-a-kind portal, and it has been a fantastic experience. I was particularly impressed by how efficiently and quickly the team always came up with creative solutions to provide us with all the functionalities within the portal we had requested.

Judith Mueller
Executive Director, Mueller Health Foundation

ValueCoders had great technical expertise, both in front-end and back-end development. Other project management was well organized. Account management was friendly and always available. I would give ValueCoders ten out of ten!

Kris Bruynson
Director, Storloft

Huge thank you to ValueCoders; they have been a massive help in enabling us to start developing our project within a few weeks, so it's been great! There have been two small bumps in the road, but overall, It's been a fantastic service. I have already recommended it to one of my friends.

Mohammed Mirza

James Kelly

Co-founder, Miracle Choice


Judith Mueller

Executive Director


Kris Bruynson



Mohammed Mirza


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