Principal Data Scientist

What will you be responsible for? 

As a Principal Data Scientist, you will be responsible to develop effective and high-quality healthcare program integrity analytics that meet business requirements. Technical abilities include following Python/R coding best practices, performance tuning, unit testing and production support. In Addition, your responsibilities include: 

• Performs advanced statistical analyses to identify patterns and trends and opportunity assessments to assist in delivering optimal healthcare management and decision making. 
• Designs data visualizations and determines the best way to present data in a clear understandable format using reports, drilldowns, tables, gauges, graphs, charts, and other intuitive graphical add-ons. 
• Experience working with Gen-AI, expertise in fine-tuning transformer-based models / LLMs- GPT, Llama, PaLM, BERT and RAG models. 
• Fine-tuning of Large Language Models (GPT/ PaLM/ Llama) to meet specific business requirements. 
• Develop and implement machine learning models using a variety of techniques (supervised and unsupervised learning models including NLP, Deep learning Models, and Predictive Analytics) 
• Ensure accuracy of data and deliverables of reporting employees with comprehensive policies and processes. 
• Manage and optimize processes for data intake, validation, mining, and engineering as well as modelling, visualization, and communication deliverables. 
• Prepares and delivers results to leadership with analytic insights, interpretations, and recommendations. 
• Understanding data storage and data sharing methods. 
• Understanding healthcare business operations. 
• Strong proficiency in: Python, PySpark, SQL, R & have experience in machine learning libraries & frameworks such as TensorFlow, PyTorch, or Keras. 
• Deep expertise in traditional as well as modern statistical & ML techniques like regression, support vector machines, Regularization, Boosting, Random Forests & other ensemble methods. 
• Proficiency in developing NLP models using: Nltk, spacy, Genism , Word 2 Vec , Seq 2 seq , transformers , BERT etc. 
• Prior hands-on experience in analysing large and complex data sets, data reliability analysis, quality controls and data processing, with focus on model validation practices.  

Who are we looking for? 

• 12 yrs. of professional work experience preferable in management consulting or high growth start-ups preferably in healthcare and 8-12+ years of experience in a data analytical role. 
• Bachelor's degree in mathematics, statistics, healthcare administration, or related field. 
• Master's degree advantageous. 
• 5+ years of experience in Python, SQL, R, SAS 
• Designing, developing, and implementing AI/ generative AI models & algorithms to solve complex problems and drive innovation across organization. 
• Lead all stages of AI/ML solutions implementation: Gathering business requirements & understanding, data requirements for the solution build, any constraints (data /business), data exploration/solution design, machine learning models development, active collaboration with model risk team to ensure high quality model deployment & minimize enterprise risk. 
• Lead the implementation of AI solutions to deliver business impact with focus on value, success criteria alignment, scalability, and operationalization. 
• Collaborating with cross-functional teams to define project requirements and objectives, ensuring alignment with overall business goals for integration, sign-off and deploying machine learning models into production. 
• Developing clear and concise documentation, including technical specifications, user guides, and presentations, to communicate complex AI concepts to both technical and non-technical stakeholders. 
• Engage team members, project managers & business stakeholders in the analysis and interpretation of experimentation results & ensuring feedback is incorporated as appropriate into models. 
• Drive best practices throughout development process and publish learnings/feedback for continuous learning. 
• Lead/drive and accelerate innovations in discovery phase via insights, frameworks, causal inference solutions and machine learning prototypes via POCs. 
• Refine standards and processes for AI solution development & implementation in close collaboration with data science leaders and team in the US. Ensure adherence to the industry / enterprise standards and best practices. 
• Develop and institutionalize best practices and re-usable components, contribute to research and experimentation efforts. 
• Lead, coach, support, and mentor data scientists in the team review their work as required, provide adequate guidance, feedback to help them achieve their goals and do right for Enterprise. 
• Participate in talent acquisition activities to build strong talent pool of Data Scientists.