Senior Data Scientist – Forecasting and Predictive Analytics

Job Description

A leading organization is seeking a Senior Data Scientist to support advanced forecasting and assumptions analytics initiatives within a healthcare-focused environment. This position combines data science, analytics, and data engineering expertise to evaluate forecasting performance, improve predictive accuracy, and generate actionable business insights.

The successful candidate will work with large datasets, develop predictive models, assess the value of improved forecasting assumptions, and help drive data-informed decision-making.

Responsibilities

Forecasting and Predictive Modeling

• Design and develop predictive models to improve forecasting accuracy and utilization projections.
• Analyze historical trends, market behaviors, and performance patterns.
• Apply statistical methods and machine learning techniques to enhance prediction quality.
• Validate model performance and recommend ongoing improvements.
• Monitor forecasting outcomes and refine analytical approaches based on results.

Analytics and Business Impact Assessment

• Evaluate current forecasting assumptions and identify areas for improvement.
• Compare historical forecasts against actual outcomes to assess accuracy.
• Measure forecast variance, risk exposure, and opportunity impact.
• Quantify the business value associated with improved prediction accuracy.
• Present analytical findings and recommendations to business stakeholders.
• Translate complex data insights into clear business recommendations.

Data Engineering and Technical Execution

• Extract, transform, and analyze large datasets using SQL and Python.
• Build reusable data processes, workflows, and analytical pipelines.
• Support model deployment and operationalization activities.
• Contribute to scalable analytics solutions within cloud-based environments.
• Ensure data quality and consistency across analytical processes.

Cross-Functional Collaboration

• Partner with analytics, forecasting, and strategic planning teams.
• Collaborate with technical and business stakeholders on solution design.
• Participate in analytical reviews, modeling discussions, and continuous improvement initiatives.
• Support data-driven decision-making across the organization.

Required Experience and Skills

Professional Experience

• 5 to 7+ years of experience in data science, advanced analytics, machine learning, or a related field.
• Demonstrated experience developing predictive analytics and forecasting solutions.
• Proven track record of delivering models that support measurable business outcomes.

Technical Skills

Python

• Strong proficiency in Python.
• Data analysis and data manipulation.
• Statistical modeling.
• Machine learning development.
• Model validation and performance evaluation.

SQL

• Strong proficiency in SQL.
• Complex query development.
• Data transformation and preparation.
• Query optimization and performance tuning.
• Large-scale data analysis.

Data Science and Modeling

• Predictive modeling.
• Forecasting methodologies.
• Statistical analysis.
• Machine learning techniques.
• Feature engineering.
• Model performance assessment and optimization.

Cloud and AI Tools

• Experience working within cloud-based environments.
• Ability to use AI tools to enhance productivity and analytical workflows.
• Familiarity with tools such as Claude, ChatGPT, Copilot, or similar AI solutions.
• Understanding of practical AI applications within analytics and modeling processes.

Preferred Experience

• Experience in healthcare, pharmaceutical, payer, or life sciences analytics.
• Knowledge of market transition forecasting and utilization trend analysis.
• Experience with time series analysis and forecasting techniques.
• Exposure to machine learning production environments.
• Familiarity with AI-enabled analytics workflows.

FAQ

1. What does a Senior Data Scientist specializing in forecasting and predictive analytics do?

A Senior Data Scientist develops advanced statistical and machine learning models to forecast future outcomes and identify patterns in complex datasets. The role combines data analysis, predictive modeling, experimentation, and business insight to support strategic and operational decisions.

2. What types of forecasting problems does this role typically address?

Forecasting work can include demand prediction, revenue forecasting, customer behavior, resource planning, utilization trends, and operational capacity planning. The specific models depend on the business problem, available historical data, and required forecast horizon.

3. Which statistical and machine learning techniques are commonly used?

Techniques may include time-series forecasting, regression, classification, gradient boosting, ensemble methods, clustering, and other machine learning approaches. Senior Data Scientists select and validate methods based on data characteristics, prediction objectives, interpretability requirements, and business constraints.

4. How is Python used in forecasting and predictive analytics?

Python is commonly used for data preparation, exploratory analysis, statistical modeling, machine learning, feature engineering, and model evaluation. Libraries such as pandas, NumPy, scikit-learn, and specialized forecasting frameworks can support the development and validation of predictive models.

5. What role does SQL play in this position?

SQL is used to retrieve, join, transform, and analyze data from relational databases and analytical data platforms. Strong SQL skills are important for preparing reliable datasets and investigating the historical patterns required for forecasting models.

6. How are forecasting models evaluated?

Models are evaluated using appropriate statistical and business metrics such as MAE, RMSE, MAPE, precision, recall, or other problem-specific measures. Data Scientists also use validation strategies and backtesting to assess how well models perform on unseen or future data.