Senior Data Scientist (Forecasting & Assumptions Analytics)

Position Summary

We are seeking a Senior Data Scientist to join a healthcare analytics team focused on improving the accuracy of critical business and market assumptions that drive forecasting and decision-making. This role sits at the intersection of data science, analytics, and data engineering, requiring strong modeling expertise as well as the ability to work hands-on with data to develop scalable solutions.

The ideal candidate will analyze existing forecasting assumptions, quantify business impact from improving prediction accuracy, and build models that enhance decision quality across the organization.

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Project Overview

What Type of Project Will This Person Work On?

This role will support a healthcare/pharmaceutical forecasting initiative focused on predicting market behavior and utilization trends.

Example Use Case: When a branded pharmaceutical drug loses patent protection and generic alternatives enter the market, the team forecasts:

•             How much utilization will shift from the brand drug to generic alternatives

•             The timing and magnitude of market adoption

•             Financial and operational impacts of these transitions

•             Accuracy of current forecasting assumptions

The team evaluates whether existing assumptions are accurate, identifies areas where assumptions can be improved, measures the potential business value of improved predictions, and develops models to increase forecasting accuracy.

Key Project Phases

Phase 1: Assumption Analysis

•             Evaluate current business and forecasting assumptions

•             Assess historical accuracy versus actual outcomes

•             Identify gaps, biases, and opportunities for improvement

Phase 2: Value Attribution

•             Quantify potential business impact of improving assumption accuracy

•             Measure risk, opportunity, and forecast variance

•             Develop business cases for modeling initiatives

Phase 3: Predictive Modeling

•             Build and deploy models to improve forecast accuracy

•             Enhance decision-making through data-driven recommendations

•             Continuously monitor and refine model performance

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Key Responsibilities

Data Science & Modeling

•             Develop predictive and forecasting models to improve healthcare and pharmaceutical utilization assumptions

•             Analyze historical patterns, trends, and market behavior

•             Apply statistical and machine learning techniques to improve forecast accuracy

•             Evaluate model performance and recommend enhancements

Analytics & Business Impact Assessment

•             Analyze existing assumptions and identify areas of underperformance

•             Quantify the financial and operational value of improved forecasting accuracy

•             Present findings and recommendations to business stakeholders

•             Translate complex analytical results into actionable insights

Data Engineering & Technical Execution

•             Extract, cleanse, transform, and analyze large datasets using SQL and Python

•             Build reusable analytics pipelines and workflows

•             Support model deployment and operationalization efforts

•             Work with cloud-based tools and platforms to enable scalable analytics solutions

Collaboration

•             Partner closely with data scientists, business stakeholders, and leadership

•             Collaborate with teams responsible for forecasting, market analytics, and strategic planning

•             Contribute to technical discussions and solution design

•             Support continuous improvement initiatives across the analytics organization

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Required Qualifications

Experience

•             5–7+ years of professional experience in Data Science, Advanced Analytics, Machine Learning, or a related field

•             Proven experience working on predictive analytics and forecasting initiatives

•             Experience delivering models that drive measurable business outcomes

Technical Skills

Python (Required)

•             Intermediate to advanced proficiency

•             Data analysis and manipulation

•             Statistical modeling

•             Machine learning development

•             Model evaluation and validation

SQL (Required)

•             Intermediate to advanced proficiency

•             Complex querying

•             Data transformation

•             Performance optimization

•             Large-scale data analysis

Modeling & Data Science

•             Predictive modeling

•             Forecasting techniques

•             Statistical analysis

•             Machine learning

•             Model performance assessment

•             Feature engineering

Cloud & AI Tools

•             Experience working within cloud environments

•             Ability to leverage AI tools to improve productivity and analytical workflows

•             Familiarity with modern AI solutions such as Claude, ChatGPT, Copilot, or similar tools is preferred

•             Not expected to build foundational LLMs, but should understand how to effectively utilize AI technologies

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Preferred Qualifications

•             Experience in healthcare, pharmaceutical, payer, or life sciences analytics

•             Understanding of brand-to-generic market transitions and utilization forecasting

•             Experience with time series analysis and forecasting methodologies

•             Exposure to machine learning production environments

•             Familiarity with modern AI-enabled analytics workflows