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Ascension LLC is seeking a Lead Data Scientist to guide advanced analytics, artificial intelligence, machine learning, statistical modeling, and predictive analytics activities in support of a federal oversight mission. This position is designed for a senior analytics professional who can translate complex data into mission-relevant insights, design defensible models, explain findings to technical and non-technical audiences, and help the customer identify patterns, trends, and anomalies that may indicate fraud, waste, abuse, operational risk, or other issues requiring further review.

The ideal candidate will bring strong technical depth in data science and AI/ML, sound judgment in model selection and validation, and the ability to work closely with project managers, data engineers, BI analysts, auditors, evaluators, investigators, and government stakeholders. This role is important because the customer requires data analytics support that improves the efficiency, timeliness, and quality of audits, evaluations, investigations, and internal oversight activities. The PWS specifically calls for data modeling, statistical analysis, predictive analytics, machine learning solutions, data quality, documentation, knowledge transfer, and continuous improvement across analytics capabilities.

Summary of the Contractor Role

The Lead Data Scientist will serve as a senior technical advisor and hands-on analytics lead responsible for designing, developing, validating, documenting, and improving data science solutions that support the customer’s oversight mission. The role requires the ability to develop actionable AI/ML and statistical solutions while maintaining transparency, interpretability, and practical applicability to government decision-making. The PWS identifies a broad data science scope that includes statistical analysis, advanced analytics, predictive analytics, natural language processing, time series analysis, network analysis, geospatial analytics, computer vision, and big data technologies.

The candidate should be self-driven, analytical, detail-oriented, and comfortable operating in a complex federal environment where requirements may evolve as new oversight priorities emerge. The Lead Data Scientist must be able to scope analytical problems, evaluate data readiness, recommend appropriate methodologies, build or oversee model development, validate results, document assumptions and limitations, and communicate findings in plain language for government stakeholders. The role will also support responsible AI/ML governance, including model monitoring, drift detection, retraining procedures, version control, bias detection, fairness considerations, transparency, and compliance with data privacy and security requirements.

Anticipated Day-to-Day Activities

The Lead Data Scientist will be expected to:

• Lead advanced analytics initiatives that apply AI, machine learning, statistical modeling, predictive analytics, and computational methods to complex federal oversight data.
• Develop predictive models, classification models, anomaly detection techniques, risk scoring approaches, and other analytical methods to identify trends, patterns, and outliers.
• Design statistical analysis plans, sampling approaches, hypothesis tests, regression models, confidence intervals, and multivariate analyses that support defensible audit, evaluation, and investigative conclusions.
• Evaluate data quality, completeness, relevance, provenance, and limitations before model development or statistical analysis begins.
• Select appropriate modeling techniques based on customer objectives, data maturity, interpretability needs, security constraints, and operational use cases.
• Build and/or oversee machine learning models using tools such as Python, R, SQL, Jupyter, scikit-learn, TensorFlow, PyTorch, SAS, SPSS, or equivalent technologies.
• Apply natural language processing methods for text analysis, topic extraction, entity recognition, classification, sentiment analysis, and summarization where appropriate.
• Support time series forecasting, trend analysis, and anomaly detection for temporal data.
• Support network analysis, geospatial analytics, computer vision, or big data analytics when required by customer use cases.
• Document model assumptions, data sources, limitations, validation methods, monitoring procedures, governance controls, and user guidance.
• Create visual artifacts, charts, model explainability outputs, and executive-ready summaries to demonstrate analytical value and impact.
• Collaborate with business intelligence analysts to translate analytical findings into dashboards, reports, data stories, and decision-support products.
• Collaborate with data engineers to ensure data structures, pipelines, transformations, and quality checks support advanced analytics and repeatable model execution.
• Review model outputs for accuracy, objectivity, bias, fairness, interpretability, and mission relevance.
• Conduct peer reviews of analytical methods, models, scripts, reports, and technical documentation.
• Brief findings to technical and non-technical stakeholders, including senior government leaders, using clear and concise language.
• Support knowledge transfer by developing user guides, technical documentation, training materials, and walkthrough sessions for government staff.
• Maintain version control, reproducibility standards, model monitoring procedures, and documentation repositories.
• Advise the customer on emerging AI/ML technologies, benefits, risks, governance considerations, and practical implementation pathways.
• Support continuous improvement of analytics processes, model lifecycle management, documentation practices, and quality control procedures.