Data Scientist Manager

Core Responsibilities

• Implement and promote the adoption and effective use of artificial intelligence, LLMs, machine learning, automation tools, and emerging analytical technologies.
• Oversee the analysis, visualization, and interpretation of various federal datasets.
• Manage delivery of analytical products, reports, dashboards, and data-driven recommendations to clients and stakeholders.
• Provide leadership across the data lifecycle, including data intake, storage, governance, synthesis, automation, and analytical development.
• Establish and implement policies, processes, and procedures that support effective data management, analytics delivery, and team performance.
• Lead the development and implementation of QA/QC protocols, defect tracking processes, and data governance standards to ensure data quality, integrity, security, and availability.
• Serve as the primary technical and operational point of contact for clients, stakeholders, and project leadership.
• Manage project priorities, resource allocation, staffing, workload balancing, and delivery schedules to ensure successful project outcomes.
• Lead and manage teams of data analysts, data scientists, data engineers, and other technical staff across multiple projects and workstreams.
• Mentor, coach, and develop team members, providing technical guidance, performance feedback, and career development support.
• Evaluate AI-enabled tools and methodologies to improve data processing, coding efficiency, workflow automation, and analytical capabilities.
• Support proposal development, technical solutioning, project planning, and business development activities.
• Collaborate with cross-functional teams including project leadership to communicate insights and translate complex analytical findings into actionable recommendations.
• Promote continuous improvement, innovation, and adoption of industry best practices across analytics and data science teams.

Position Requirements & Preferences

• Required Qualifications:
• Bachelor's degree plus 9 years of relevant experience, Master's degree plus 7 years of relevant experience, or PhD plus 4 years of relevant experience.
• Strong understanding of AI tools and LLMs, machine learning concepts, analytical methodologies, and emerging technologies relevant to data analytics and data science.
• Demonstrated experience leading and managing teams of data analysts, data scientists, data engineers, and other technical professionals.
• Experience managing analytics projects, contract tasks, and client-facing deliverables.
• Strong expertise in statistical, data analytics, and data management languages including SQL, Python, SAS, and/or R.
• Strong experience with cloud platforms including AWS, Azure, or GCP.
• Experience working with relational databases and modern data management platforms including Databricks and Snowflake as well as with associated technologies including Docker, Kubernetes, MLFlow, or similar platforms.
• Experience implementing data governance, QA/QC, and data lifecycle management practices.
• Stay current on emerging trends, technologies, and best practices in data science, machine learning, and artificial intelligence and apply these learnings to project work and employee mentorship.
• Experience evaluating and implementing AI-enabled processes, automation tools, and analytical efficiencies including understanding the pros and cons of various AI LLMs.
• Experience working in a consulting environment, including direct client engagement and stakeholder communication.
• Strong technical writing skills with the ability to produce clear, concise, and well-documented analyses, reports, and presentations.
• Critical thinker with strong leadership, communication, collaboration, and problem-solving skills.
• Experience supporting proposal efforts, technical solutioning, and business development activities.
• Ability to obtain and maintain public trust access for federal information systems and successfully complete a federal background check.
• Preferred Qualifications:
• Experience managing federal data and analytics programs particularly for CMS contracts.
• Experience with data linking methodologies, including fuzzy matching techniques such as Levenshtein distance, Jaccard similarity index, and Jaro-Winkler similarity index.
• Proficiency with version control tools including Git, Bitbucket, and SourceTree.
• Proficiency with agile project management principles and tools, including Jira and Confluence.
• Experience with leading analytic code translation and modernization efforts from SAS to Python including code evaluation and refactoring.
• Experience developing reports and dashboards, reporting solutions, workflow automation, and integrated data products.
• Experience supporting data science, predictive analytics, machine learning, or AI-enabled initiatives.
• Experience managing multidisciplinary teams in Agile project environments.