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The healthcare industry is the next great frontier of opportunity for software development, and Health Catalyst is one of the most agile and influential companies in this space. We are working on solving national-level healthcare problems, and this is your chance to improve the lives of millions of people, including your family and friends. Health Catalyst is a fast-growing company that values smart, hardworking, and humble individuals. Each product team is a small, mission-critical team focused on developing innovative tools to support Catalyst’s mission to improve healthcare performance, cost, and quality.

Health Catalyst is expanding and maintains a large suite of Improvement Apps that contribute to healthcare analytics and process improvement solutions. This includes products that manage the care of health system populations, better serve patients at the point of care, reduce health system costs, and reduce clinician workload.

Job Summary:

As a lead data scientist, you will be working with diverse Improvement Apps, software engineering team designing, developing, and maintaining various platforms that serve internal HCAT team members, clinicians, and patients. You will rely on Test-Driven Development to safely enhance and refactor our system, shipping production code multiple times per week. And you will go to bed each night with the comfort that your code is improving outcomes for patients.

If you love…

Help drive clarity and prototype individual features or problems

Knowledge of architecture patterns and the ability to design and complete features / tasks that are 50-60% well defined.

Can discern where gaps can be filled in without consulting a Product Manager or another programmer and can judge when a consultation is needed.

Work is reviewed with the occasional need for material direction or implementation changes

Seeks and provides guidance via PR reviews, pair-programming and other interactions with Engineers and Product Managers

It is second nature to develop high code quality standards balanced with the needs of real-world customer timelines.

Possesses a passion and drive to deliver exceptional products and follows established patterns and approaches within existing code bases with ease.

Takes ownership of learning and growth

Capitalizes on internal and external opportunities for learning.

Identifies gaps in knowledge/skills and seeks ways to close those gaps (self-guided learning, pairing, seeking guidance for yourself and developing guidance for less experienced members of the team)

Periodic On Call Rotation





Ability to communicate with Customer Success about customer issues that are escalated to Engineering and help quantify customer impact.

Can Respond quickly to operational emergencies, find short term resolutions and plan long term fixes to avoid similar issues in the future.

What you own in the role:

- Lead the end-to-end design, development, and production deployment of advanced machine learning, statistical, and causal inference models — owning model architecture decisions, validation frameworks, and performance benchmarks that directly address complex business problems at scale.
- Define and govern data science SQL standards across the team, including complex query optimization, feature store design, and analytical data modeling on platforms such as Databricks SQL, Azure Synapse, or Snowflake to support scalable model pipelines.
- Oversee and guide the team's data wrangling, feature engineering, and EDA practices using Python, establishing reusable preprocessing pipelines and feature libraries that accelerate experimentation and ensure consistency across projects.
- Architect and optimize production-grade ML solutions using PyTorch, TensorFlow, and scikit-learn — driving model selection, hyperparameter tuning strategies, and algorithmic improvements while evaluating trade-offs between interpretability and performance.
- Act as the primary bridge between business stakeholders and data science teams — decomposing ambiguous business challenges into well-scoped data science problem statements with clear success metrics, deliverables, and ROI framing.
- Lead cross-functional collaboration with data engineering, product, and ML engineering teams to design and deliver end-to-end data-driven solutions — from data acquisition and model development through to deployment and feedback loops.
- Define the team's data visualization and stakeholder communication standards, overseeing the development of executive-level dashboards and insight reports using tools such as Power BI, Plotly, or Streamlit to drive data-informed decision making.
- Establish and enforce MLOps best practices across the team — including experiment tracking with MLflow, model versioning, CI/CD integration for ML pipelines, automated testing, and monitoring for data drift and model degradation in production.




- Mentor and technically develop junior and mid-level data scientists through structured code reviews, model design sessions, and knowledge-sharing initiatives — building a high-performing, self-sufficient data science practice.

What you bring to this role:

- Bachelor’s/Master’s/PhD in Computer Science, Data Science, Statistics, Mathematics, or related field.
- 9+ years of experience as a Data Scientist, with a strong focus on Python programming.
- Expertise in Python-based libraries: Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch, Matplotlib/Seaborn, Statsmodels.
- Solid knowledge of statistical modeling, hypothesis testing, and machine learning techniques.
- Hands-on experience in data cleaning, feature engineering, and model evaluation.
- Strong experience with SQL and working with structured/unstructured datasets.
- Familiarity with big data platforms (Spark, Hadoop) and cloud environments (AWS, GCP, or Azure).
- Strong problem-solving, analytical, and communication skills.
- An understanding of healthcare data is a plus, but not a requirement
- Experience with deep learning, NLP, or recommendation systems.
- Familiarity with MLOps practices (MLflow, Kubeflow, Airflow).
- Knowledge of data visualization tools (Tableau, Power BI, Plotly).

You may also bring:

Experience with cloud infrastructure and architecture patterns, either Azure or AWS preferred.

Software development experience within healthcare IT and understands key data models (clinical, claims, financial, etc.) and interoperability standards such as HL7v2, CDA, EMR, and FHIR

Knowledge of healthcare compliance and how it applies to Application Security

Agile/Scrum software development practices

Business Intelligence or Data warehousing experience

Preferred Experience and Education:

BS/BA or MS in Computer science, information systems, or other technology/science degree.

A minimum of 9+ years of experience in building commercial software, SaaS, or digital platforms.

Equal Employment Opportunity has been, and will continue to be, a fundamental principle at Health Catalyst, where employment is based upon personal capabilities and qualification without discrimination or harassment on the basis of race, color, national origin, religion, sex, sexual orientation, gender identity, age, disability, citizenship status, marital status, creed, genetic predisposition or carrier status, sexual orientation or any other characteristic protected by law.. Health Catalyst is committed to a work environment where all individuals are treated with respect and dignity..

Apply on Kit Job: kitjob.in/job/44m1kf

📌 Lead Data Scientist (Raipur)
🏢 Health Catalyst
📍 Raipur