Data Scientist – Healthcare Analytics

The job is a full-time, permanent role with flexible working hours (overlap with EST required)

## Job Summary
We need an experienced Data Scientist to join us for the long term and lead the creation of data-driven healthcare solutions at our company. The best person for the job knows mathematical modeling, statistics, Machine Learning (ML), and Artificial Intelligence (AI), and has applied them to health data such as clinical records, claims, and cost data. They are also used to working with current frameworks and large language models (LLMs). You will use your strong problem-solving, critical thinking, and research skills to plan and carry out complete data science projects that improve health outcomes and manage the cost of care, try out new methods, share your results, and help build intellectual property that becomes a lasting asset of the company.
## Some of your main duties
Research and new ideas:
- Learn more about statistical and machine learning methods for health data (such as survival analysis, risk adjustment, and causal inference), as well as modern LLM frameworks and APIs (for example LangChain or equivalent) for working with clinical text.
- Encourage new ideas by testing, proving, and studying the viability of new data-driven healthcare products or features.
- Keep up with new technologies and trends in healthcare analytics and AI to make sure the company stays on the cutting edge.
End-to-End Project Execution:
- Take charge of healthcare data science projects from the time they are first thought of to the time they are put into action. This includes gathering requirements with clinical and business stakeholders, modeling, and keeping an eye on performance over time.
- Partner cross-functionally with Engineering, Product, Clinical, and Actuarial or Finance teams to define backlogs, prioritize work, and deliver data solutions incrementally.
- Ensure all models and data pipelines adhere to strong quality control, governance, privacy, and continuous improvement standards, including validation for bias and fairness across patient groups.
Model Development and Deployment:
- Use both traditional statistical and machine learning (ML) methods and cutting-edge artificial intelligence (AI) to design, build, and improve predictive and prescriptive models, such as health risk, cost, utilization, and disease progression models.
- Use best practices for feature building and data transformation on complex health data from many sources, including diagnosis and procedure codes, lab results, pharmacy, and billing records.
- Use the best frameworks and methods (like clinical NLP, prompt engineering, and LLM fine-tuning) to solve hard data problems, such as coding free-text diagnoses and linking patient records.
Mentorship and technical leadership:
- Lead by example and push for data-driven decisions across clinical, business, and technical teams.
- Be a mentor to peer data scientists and AI engineers, helping them learn new skills and advance in their careers.
- Push for best practices in code quality, version control, and being able to make changes again and again.
Design and Validation of Studies and Experiments:
- Do thorough tests and observational studies (A/B testing, hypothesis testing, matched controls, and other causal methods) to confirm how well models and health programs work and how they affect outcomes and costs.
- Use advanced statistical methods and data visualizations to share results with clinicians, actuaries, business leaders, and other people who don't have the same level of technical knowledge.
Knowledge Sharing and Intellectual Property:
- Put together and talk about study results at company seminars, outside conferences, or healthcare and industry events.
- Write for academic, healthcare, and business journals and look for ways to protect new ideas.
- Write down findings, methods, assumptions, and best practices so that models can be explained and reproduced over time.
Continual Improvement:
- Find ways to make data pipelines better, such as how they receive, handle, store, and analyse health data, and then put those ideas into action.
- Use DevOps and MLOps to make the model creation lifecycle easier, monitor models for drift, and make sure that deployments happen reliably.

Skills and qualifications needed

- Education: Master's or Ph.D. in statistics, biostatistics, epidemiology, computer science, math, data science, or a related field, or at least three years of work in a similar role, ideally with healthcare, insurance, or claims data.
- Technical Knowledge – Programming : Skill with Python, SQL, and related tools (such as NumPy, Pandas, scikit-learn, TensorFlow, and PyTorch).
- Statistics & Math: Strong knowledge of advanced math concepts (such as linear algebra, calculus, and optimization) and statistical ideas (such as probability, hypothesis testing, regression, survival analysis, and causal inference).
- Machine Learning: Supervised, unsupervised, and deep learning models that have been built and used successfully in the past, with attention to calibration, explainability, and fairness.
- Healthcare Data: Experience with health records or claims data and their standards and code sets (such as ICD-10, SNOMED, or FHIR).
- LLM & Generative AI Tools: Know how to use LangChain, LangGraph, OpenAI, Claude, Gemini, LLAMA, and other similar tools for large language model projects, including work with clinical text.
- Infrastructure and Data Handling: Knowledge of data engineering techniques, such as ETL processes, big data solutions, and cloud-based platforms (AWS, Azure, or GCP), and of handling sensitive health data securely. It would be helpful to know about containerization (Docker) and management (Kubernetes).

- Critical Thinking and Problem-Solving: The skill of being able to break down difficult issues into manageable steps and plan experiments to test theories. Able to work with uncertainty and quickly come up with new ideas.

- Communication and teamwork: You should be able to communicate clearly both in writing and in person, and you should be able to turn complicated studies into insights that are useful for clinicians and the business.
- Leadership: Proven ability to lead cross-functional teams, get everyone on the same page, and get everyone to agree on something.
- Long-term Commitment: Motivated to own models and grow with the team over many years.
- Publications and Patents: It's a plus if you have a history of writing in peer-reviewed journals, giving talks at conferences, or patenting your own work, particularly in healthcare.

Why Should You Join?

- Impactful Work: You'll be able to lead projects with a lot of attention and create new data products that improve health outcomes and give the whole organization real value.
- Cutting-Edge Tools & Research: New AI frameworks, large language models, and cutting-edge computing tools are at your disposal.
- Culture of Innovation: We urge people to try new things, learn from their mistakes, and share what they know through patent applications and publications.
- Professional Growth: a long-term role with a mentor, ongoing training, a path to lead as the team grows, and chances to show off your skills at classes, conferences, and meetups.
- Flexible Working Hours : Tailor your work hours to fit your lifestyle and enhance your work-life balance.
- Work From Home : Enjoy the flexibility of working from home to increase productivity and comfort.
- Laptop Provided : A high-performance laptop to help you be productive and efficient in your work.

We want to hear from you if you are interested in AI and healthcare, eager to learn, and ready to use cutting-edge technology to solve hard problems.