Data Scientist

• Responsible for the technical delivery both as a project manager and site lead for mission critical commissioning projects with minimal supervision while completing assignments that require independent judgement and evaluation.
• Maintain effective communication with clients, contractors and other project team members.
• Develop and maintain client relationships.
• Manage project specific commissioning plans, commissioning procedures, checklists, test scripts, and schedules for Level 1 through Level 5 commissioning activities.
• Lead onsite teams to perform functional performance tests on mechanical, plumbing, and electrical systems.
• Provide project management and coordination to meet client expectations for technical excellence, budget and schedule.

Data Center Commissioning Project Manager

• Lead the data science services contract team in providing analytical support to address complex business questions and mission needs
• Meet with program leaders, government stakeholders, and technical teams to identify priorities, understand business processes, and guide delivery of innovative data analysis techniques
• Collaborate with data scientists, analysts, engineers, architects, and application developers to support development of data science reports, machine learning models, dashboards, and other analytical solutions
• Assist the team in articulating mission gap problems, process inefficiencies, and operational challenges that can be addressed through data science, business intelligence, or automation
• Oversee implementation of analytical solutions recommended by the team, including data science reports, machine learning models, visualizations, and decision-support products for agency leaders and stakeholders
• Manage project scope, staffing, schedules, risks, deliverables, and performance to ensure contractual obligations are fulfilled and quality standards are met
• Develop, maintain , and communicate work plans, project schedules, roadmaps, backlogs, status reports, and other management artifacts
• Provide expert direction and guidance to subordinate consultants, data scientists, and technical team members
• Serve as the primary point of contact to the Contracting Officer’s Representative and support ongoing coordination with the government Program Manager and other stakeholders
• Manage and control project funds and resources in accordance with contract requirements
• Support Agile delivery activities, including sprint planning, backlog management, prioritization, stakeholder demonstrations, and continuous improvement
• Ensure technical concepts, analytical findings, project risks, and delivery recommendations are communicated clearly to both technical and non-technical audiences
• Support workforce transformation activities by helping teams present data science concepts, analytical products, and lessons learned to large audiences and varied stakeholder groups

Senior Project Manager – Data Science Services

• As a Consumer Product Data Scientist, you will be using the Databricks platform to analyze complex datasets, identify trends, uncover patterns, and provide meaningful interpretations for a highly visible online product.
• You will leverage skills in big data collection, cleaning, and exploratory analysis to discover patterns, anomalies, and relationships using statistical methods and visualization techniques.
• Requires strong experience applying statistical tests and models to validate hypotheses and identify significant trends in customer retention and churn.

Consumer Product Data Scientist

• Working closely with practice group leadership and team members in both Professional Development and Legal Talent, the Associate Development Manager is primarily responsible for supporting associate staffing, utilization, integration and development.
• Work with practice group leaders and partners in charge of staffing to manage the work allocation process for associates in the group
• Monitor workloads and professional development of associates and help allocate new matters in a manner which ensures an equitable distribution of assignments consistent with the clients’ needs and the attorneys’ individual professional development goals
• Track, aggregate and analyze associate metrics, including availability information and utilization rates.
• Partner with practice group leaders and associates to ensure that individual goals relating to productivity are achieved and practice group productivity is maintained
• Coordinate with practice group leaders and other supervising partners to identify staffing needs and hiring targets; partner with the Legal Talent team to ensure hiring efforts are supporting the goals and needs of the group
• Support the associate performance review process by working with practice group leaders and Associate Review Committee members to ensure quality reviews are delivered in a timely fashion
• Help monitor associate evaluations, prepare review delivery scripts and facilitate the delivery of formal reviews.
• Work with associates to implement feedback received, collaborating with Professional Development team members to provide coaching, where necessary
• Meet regularly with practice group leaders to identify high performing associates and associates who may be having specific challenges so that appropriate mentoring, training and other actions can be taken to address specific circumstances in a timely manner.
• Facilitate frequent feedback between partners and associates
• Partner with Legal Talent and other internal administrative departments to facilitate successful attorney on-boarding and integration, including conducting department orientation for new hires and building robust onboarding schedules.
• Help conduct exit interviews such that the firm can leverage departing associate feedback to improve the associate experience
• Collaborate with Professional Development and Legal Education team members to develop, enhance and implement legal and professional skills training programs and initiatives to support associate development
• Collaborate with the firm’s Attorney Licensing and Compliance team members to ensure associates obtain and maintain their licensure requirements and engage in continuing legal education aligned with development goals
• Collaborate with the firm's Diversity and Inclusion team to champion and implement initiatives and events.
• Promote best practices in commitment to an inclusive culture and serve as a resource for diverse attorneys
• Collaborate with HR/Wellness teams to promote the wellness of associates
• All other duties as assigned or required

Associate Development Manager – Cyber, Data, Privacy Group

• Responsible for mining big data, interpreting and analyzing complex data sets
• Develops and deploys predictive models to analyze company performance and improve customer experiences
• Applies statistical techniques and machine learning algorithms to identify trends within Mazda data
• Collaborates closely with data engineers, analysts, and business stakeholders to integrate data-driven solutions

• Own assigned projects and workstreams from initiation through closeout.
• Develop and maintain project execution plans, schedules, and milestone tracking.
• Drive project progress and accountability across internal teams, vendors, consultants, and external partners.
• Coordinate project activities to ensure alignment with program objectives, timelines, and deliverables.
• Facilitate project meetings, track action items, and follow through to completion.
• Build and maintain project-level schedules aligned to the broader program master schedule.
• Monitor project dependencies and identify impacts to adjacent workstreams.
• Support integration of project schedules into the overall program plan.
• Escalate schedule risks and conflicts proactively while recommending mitigation strategies.
• Coordinate activities across engineering, design, construction, infrastructure, and operational stakeholders.
• Manage communication between project teams, vendors, consultants, and leadership.
• Ensure all parties understand scope, responsibilities, milestones, and expectations.
• Support cross-functional collaboration across the QC Ecosystem and external delivery partners.
• Track project budgets, scope, schedules, risks, issues, and progress.
• Maintain accurate project documentation including meeting notes, decision logs, change requests, and status reports.
• Provide regular project updates and executive-ready reporting to Program Leadership.
• Contribute to phase-gate reviews and readiness assessments.
• Identify project-level risks, constraints, and issues early.
• Drive resolution through collaboration and ownership.

Project Manager – Data Center Program Delivery

• Collaborate with product and/or business owners, data engineers, and platform teams to understand business needs and current capabilities, data availability, and alternative uses.
• Develop and advise on the implementation of new statistical modeling capabilities to solve technical business problems and inform the team's technical direction.
• Apply and develop advanced analytic capabilities to enhance the delivery of business applications, and support the integration of data and statistical models or algorithms.
• Apply extensive knowledge of practices in research and testing for product development, deployment, and maintenance.
• Design new modeling applications to support risk measurement, financial valuation, decision making, and business performance for parts of products or initiatives.
• Design data visualizations, technical documentation, and non-technical presentation materials to communicate new ideas and high-impact solutions to business partners
• Apply understanding of relevant business context to interpret model results, monitor performance, and assess risks.
• Communicate complex technical subject matter clearly and concisely, both verbally and through written communication, such as white papers, review reports, or workpapers

Data Science Advisor

• Reporting directly to the Senior Director of Sponsored Projects Financial Operations (SPFO).
• Leads SPFO’s operational intelligence, portfolio management, and workforce planning functions in support of the University’s sponsored research enterprise.
• Develops and maintains structured award portfolio assignments.
• Monitors operational workload and portfolio complexity.
• Supports data-driven operational planning and advances workflow optimization initiatives across SPFO.
• Maintains direct operational responsibility for a designated portfolio of sponsored awards, including sponsor billing, financial reporting, award setup review, and award closeout activities.
• Develops operational reporting, dashboards, and portfolio tracking tools to support data-driven decision-making.
• Collaborates with SPFO leadership and operational teams to strengthen workflow coordination and operational best practices.

Manager, Sponsored Data and Portfolio Management

• Leading data validation and risk management initiatives to support client objectives
• Utilizing advanced analytics techniques to extract insights from complex datasets
• Developing and implementing data quality improvement plans to enhance data integrity
• Managing and mentoring teams to deliver data-driven solutions and meet client expectations
• Conducting exploratory data analysis to identify trends and inform strategic decision-making
• Building and maintaining data pipelines and integration processes for seamless data flow
• Designing and deploying data visualization dashboards using tools like Power BI and Tableau
• Collaborating with stakeholders to address data challenges and optimize business performance
• Applying statistical modeling and predictive analytics to solve complex business problems
• Overseeing the development of scalable data solutions to support client operations

Data Validation Risk – Manager

• Assist TSD with data analytic products by providing highly skilled and authoritative expertise on analytic methods.
• Perform data quality analysis on source tables to identify any abnormalities or inconsistencies.
• Review, maintain, and provide support using all existing fraud indicators developed by TSD.
• Develop and refine repeatable processes for efficiently combining and analyzing large data sources (including relational tables, structured and unstructured datasets).
• Collaborate closely with criminal investigators to determine and execute strategies to support cases; proactively address and communicate potential data quality issues and adapt analysis to shifting case needs; adhere closely to federal rules of criminal procedure governing protected information (e.g., Rule 6(e)).
• Design, develop, and implement advanced statistical and machine learning models targeted at identifying fraud, improper payments, or non-compliance within SBA programs.
• Utilize indicators of potential fraud, improper payments, or non-compliance to develop and test predictive models.
• Assist in developing case leads for SBA OIG investigations based on model outcomes.
• Coordinate with the Data Engineering Team to ensure system architecture is designed to efficiently support machine learning models.
• Integrate and scale natural language processing methods to parse, clean, and analyze large corpora of unstructured or semi-structured text data, utilizing OCR, semantic similarity algorithms, and large language models as needed.
• Provide detailed documentation of all methodology, test models, and production models; maintain records in compliance with criminal evidentiary requirements.
• Develop visualizations and dashboards to demonstrate methodological choices, outcomes, and predictive capabilities of machine learning models; iteratively solicit and integrate end-user feedback.
• Provide outcomes and recommendations in multiple formats, including data summaries and visualizations for investigative staff, and executive summaries for SBA OIG leadership.
• Create programming and automation techniques to improve task efficiency using tools such as SharePoint, Python, Microsoft Excel, Power BI, and others.
• Identify new business questions to expand the scope of analysis and reporting.

Senior Data Scientist

• designs, builds, and scales an organization's foundational data infrastructure
• experience designing and building Data Lakes, Data Warehouses, and scalable data platforms in the cloud
• supports technical activities across the contract
• drives continuous improvement and innovation into program operations
• advises on technology alternatives related to processing, data storage, data access, application development, and enterprise architecture decisions
• establishes coding best practices and aligns complex data architectures with overarching business goals
• uses APIs to push and pull data from various data systems and platforms
• builds and optimizes ETL/ELT pipelines using glue and python
• packages, curates, and versions datasets for LM fine-tuning and model training jobs on Bedrock and Sagemaker
• enforces CUI handling requirements, automates PII detection, and maintains audit trails (e.g. Amazon Macie)

• Unlock the secrets of data sets through data science expertise
• Collaborate closely with clients to understand questions and needs
• Analyze data-rich environments to find answers
• Lead the development of algorithms and systems
• Provide insights into data meanings and applications

Data Scientist, Mid

• Collect, clean, and analyze large datasets related to position descriptions, job duties, and workforce information.
• Use statistical methods, natural language processing (NLP), and machine learning techniques to identify trends, patterns, and commonalities across job roles.
• Conduct text analytics on position descriptions to map tasks, skills, and responsibilities to appropriate job series and grade levels.
• Collaborate with human resources professionals, subject matter experts, and leadership to validate data-driven findings.
• Develop dashboards, visualizations, and reports to communicate insights in a clear and actionable manner.
• Ensure consistency, accuracy, and compliance with federal classification policies and standards.
• Provide recommendations to leadership on job reclassification and series alignment based on data analysis.
• Stay current with emerging trends in workforce analytics, HR technology, and data science.

• help turn complex data sets into useful information to solve global challenges
• work closely with clients to understand their questions and needs
• dig into data-rich environments to find the pieces of information puzzle
• guide teammates and lead development of algorithms and systems
• use tools and frameworks to turn disparate data points into objective answers
• provide a deep understanding of data, what it all means, and how it can be used

• Work closely with clients to understand their questions and needs
• Dig into their data-rich environments to find the pieces of their information puzzle
• Guide teammates and lead the development of algorithms and systems
• Use the right combination of tools and frameworks to turn sets of disparate data points into objective answers
• Advise clients as they make informed decisions using the deep understanding of the data

• Using Databricks and other platforms to analyze complex datasets
• Identify trends, uncover patterns, and provide meaningful interpretations
• Discover patterns, anomalies, and relationships using statistical methods and visualization techniques
• Validate hypotheses and identify significant trends in customer retention and churn.

• Unlock secrets held by data sets to solve global challenges
• Work closely with clients to understand their questions and needs
• Dig into data-rich environments to find pieces of the information puzzle
• Guide teammates and lead the development of algorithms and systems
• Use the right combination of tools and frameworks to turn disparate data points into objective answers
• Deliver a deep understanding of the data and how it can be used

• Define Ads ontologies and metrics for campaigns, budgets, bids, creatives, audiences, and placements.
• Build dbt models and curated marts in Snowflake with clear data contracts, tests, and SLOs.
• Ingest and enrich unstructured Ads content and publish retrieval‑ready datasets using our managed search/vector services.
• Design and evaluate retrieval workflows (RAG) with existing services for hybrid search and re‑ranking; set quality/latency targets and iterate via experiments.
• Design agent reasoning and policies on ads, including tool definitions and human‑in‑the‑loop approvals.
• Establish evaluation suites covering precision/recall, calibration, hallucination rate, latency, and cost.
• Run A/B or uplift experiments to quantify impact and guide iteration.
• Translate Ads problems into agent behaviors and own KPIs such as ROAS lift, pacing accuracy, RCA precision/recall, forecast MAPE, and time‑to‑insight.

Ads AI Analytics Lead

• Own clinical data quality across claims, EHR, and ADT: Define standards for how clinical data is structured, normalized, and validated as modeling inputs across payer claims (medical, pharmacy, eligibility), EHR data (Epic, Cerner, Athena), and real-time ADT feeds. Bring deep familiarity with EHR data formats (FHIR, HL7, C-CDA) and how data from systems like Epic, Cerner, and Athena maps to clinical reality. Hold the bar for clinical accuracy and completeness across all three sources.
• Build and ship production ML/AI models: Develop, validate, and deploy risk stratification, care gap prediction, treatment effect estimation, and LLM/foundation model applications — with rigor around leakage, calibration, fairness, and clinical face validity.
• Apply health economics and outcomes methods: Translate raw clinical and claims data into decision-grade evidence through risk adjustment, utilization measurement, cost attribution, quasi-experimental evaluation, and outcomes measurement aligned with CMS, NCQA, and MCO reporting standards.
• Advance machine and AI products: Bring senior modeling judgment to the product roadmap, owning the clinical and methodological soundness of what ships.
• Set standards and mentor: Make architectural trade-offs, drive alignment across data science, engineering, product, and clinical stakeholders, and mentor junior data scientists to raise the technical bar of the team.

Principal Data Scientist, Health Informatics

• Collect, organize, and wrangle data from SAP and non‑SAP sources; build reusable analytics and data pipelines
• Clean, validate, and preprocess structured and unstructured datasets to ensure high‑quality inputs for analysis
• Conduct statistical and exploratory analyses to identify trends, patterns, and business insights
• Develop predictive and prescriptive models using SAP Analytics Cloud Predictive or external ML frameworks integrated with SAP data
• Enhance data collection procedures to support analytic system development
• Create dashboards, KPIs, and data visualizations to clearly communicate findings to business stakeholders
• Support data reconciliation and KPI validation between source systems and analytics outputs
• Collaborate with IT, business analysts, and leadership to implement data‑driven solutions
• Design and deliver AI/ML‑based decision‑making frameworks; measure and justify model value.

Data Scientist jobs in District of Columbia

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Data Center Commissioning Project Manager

Senior Project Manager – Data Science Services

Consumer Product Data Scientist

Associate Development Manager – Cyber, Data, Privacy Group

Data Scientist

Project Manager – Data Center Program Delivery

Data Science Advisor

Manager, Sponsored Data and Portfolio Management

Data Validation Risk – Manager

Senior Data Scientist

Data Science Advisor

Data Scientist, Mid

Data Scientist

Senior Data Scientist

Data Scientist, Mid

Senior Data Scientist

Data Scientist, Mid

Ads AI Analytics Lead

Principal Data Scientist, Health Informatics

SAP Data Scientist