How is this team contributing to vision of Providence?

Marketing Analytics team empowers Marketing and Digital Experience (MDeX) team with actionable, data-driven insights and measurement tools that drive impactful decisions, identify business opportunities, maximize performance, and create a competitive advantage. We achieve this by understanding our data and business/market context, partnering with MDeX to enhance the use of analytical tools, delivering timely and accurate reports and insights, and telling compelling stories about our patients, business, and experiences through advanced data storytelling, including visualization.

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. In Addition, your responsibilities include: 

• Collaborate with stakeholders to develop models that inform marketing strategies, audience targeting, and channel prioritization. Engage in product features for marketing technologies.
• Possess strong Strategic Thought Leadership, Innovative Mindset, Communication / Collaboration, Story-telling, Critical Thinking, Problem Solving skills.
• 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. 
• Performs advanced statistical analyses to identify patterns and trends and opportunity assessments to assist in delivering optimal Marketing Investments and decision making.
• Build and validate predictive models with advanced machine learning techniques and tools to drive business value, interpreting, and presenting modeling and analytical results to technical and business stakeholders.
• Develop LLM solutions on customer data such as RAG architectures on enterprise knowledge repos, querying structured data with natural language, and content generation
• Build, scale, and optimize customer data science workloads and apply best in class MLOps to productionize these workloads across a variety of domains
• Advise data teams on various data science such as architecture, tooling, and best practices
• Strong Communicator, should be able to collaborate cross-functionally with the Strategy, Product and engineering teams to define priorities and influence the product roadmap 
• 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. 
• 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. 
• Healthcare and marketing domain business knowledge will be a plus.
• Strong proficiency in: Python, PySpark, SQL, & 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, XGBoost, & 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.

What would your week look like?

Responsible for end-to-end ownership of data science use cases right from outlining the business problem, to exploring various solutions to solve the business problem, to  building, deploying, and evaluating the solution to yield high business value and customer satisfaction.

Who are we looking for?

• 14+ 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 analytics and data science role. 
• Bachelor's degree in mathematics, statistics, healthcare administration, or related field. 
• Master's degree advantageous.
• 5+ years of experience in Python, AI & ML
• 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.