Data Scientist

• 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.

SAP Data Scientist

• Serve as the primary point of contact for At Work-related data and analytics needs
• Partner with CAPTRUST’s centralized data team to prioritize, design, and improve data solutions supporting At Work
• Help establish consistent definitions, metrics, and reporting standards across the At Work practice
• Improve data quality, clarity, and usability over time
• Support and enhance internal and client-facing reporting used by At Work teams
• Work closely with Operations to understand recurring reporting needs and reduce one-off or manual efforts
• Develop dashboards and reports that support operational planning, advisor effectiveness, and leadership decision-making
• Translate reporting outputs into insights that inform action, not just describe activity
• Develop qualitative and quantitative measures of client and participant engagement, trust, and relationship health
• Help establish ongoing feedback loops across the client and participant lifecycle, beyond episodic surveys
• Identify trends that signal strengthening relationships, stagnation, or potential breakdowns
• Support analysis related to the annual financial wellness survey and other experience-related initiatives
• Analyze patterns associated with strong long-term outcomes such as sustained engagement, retention, and expansion of advisory relationships as needs evolve
• Partner with leadership and Operations to identify where additional investment of time or resources may be needed
• Help reduce reliance on anecdotal feedback through clearer, shared insight

Data & Analytics Lead

• Rigorously investigating, evaluating, interpreting, and delivering data-driven findings from relevant and diverse data sources.
• Preparing and delivering timely and actionable tactical analysis products tailored to the needs of the customer mission.
• Transforming and analyzing raw data into actionable findings, and communicating through charts, maps, timelines, and infographics to visualize complex relationships and big data sets in a user-friendly and easy-to-understand format to support operational decision making or criminal prosecution.
• Learning, applying, and maintaining relevant subject matter expertise in one or more technical areas in support of customer missions, through analytic work, operational tactics, data handling, methodology development, command briefings, and training and consultation.
• Rapidly responding to changing circumstances requiring flexibility, time-management, and the ability to function under shifting priorities while keeping track of ongoing requirements and deadlines.
• Communicating and collaborating on analysis with both internal and external analytic colleagues and with operational customers, in person or remotely, and interacting effectively with command-level and senior leadership.

Senior Data Scientist

• Actionable Insight: Generate compelling and actionable insights from complex, multi-source marketing data sets that directly inform channel investment, campaign design, and pipeline strategy.
• Stakeholder engagement: Establish strong collaborative relationships with marketing leaders and operators across demand gen, partner, field, product marketing, and brand, delivering high-impact analytics initiatives that translate loose, evolving requirements into clear deliverables.
• Attribution & Measurement: Design, build, and maintain the attribution framework for UpGuard – spanning first and multi-touch attribution and incrementality testing – and clearly communicate the trade-offs and assumptions behind each lens.
• Channel & Program Analytics: Develop a deep, first-principles understanding of channel logic across paid media, organic, content, lifecycle, partner, and field, and build the metrics, models, and dashboards that let each program owner self-serve their performance.
• Data Products: Partner with the data engineering team to design, construct, and maintain foundational marketing data assets – translating loose marketing requirements into well-specified dbt models that the whole organisation can rely on.
• Business Intelligence: Partner strategically with marketing stakeholders to provide robust self-service and conversational analytics capabilities, using design thinking principles to build user-friendly dashboards for funnel performance, channel ROI, partner sourced pipeline, and field event attribution.
• Deep Dive analysis: Personally conduct thorough, hands-on, technical analysis to diagnose and solve the most significant marketing challenges – from channel saturation and diminishing returns to lead quality decay and campaign cannibalisation.
• Commercial Analysis: Provide ongoing operational support to the commercial growth of the organisation, connecting marketing investment to pipeline, ARR, and payback in ways that finance and the executive team trust.

Marketing Analytics Lead

• Apply machine learning and natural language processing to financial crime challenges
• Develop a thorough understanding of the business landscape
• Navigate intricate scenarios to enhance personal and technical growth
• Leverage SQL and Python for data analysis and problem-solving

Senior Associate, AML and Sanctions – Data Scientist

• Collaborate with leaders across the organization to develop and execute on a data strategy inline with ActBlue's near and long term goals, including supporting organizational growth through acquisitions and partnerships
• Foster a data centric culture by upleveling our ability to make data-informed decisions and build best in class data products that meet rigorous performance and reliability standards
• Enable and advocate for AI/ML driven products and features across the ActBlue Platform through building scalable machine learning infrastructure that serves production environments and identifying new opportunities to deploy data science and analytics capacity in service of our mission
• Oversee our Data Science, Data Analytics, Data Engineering, and Data Platform teams in a fast-paced, mission-driven environment
• Guide the evolution of our data systems, tools, and architecture to meet the organization's needs, including data consolidation, governance frameworks, and unified data models across multiple entities
• Ensure high-quality communication and a stakeholder-centric approach between our data teams and their internal customers, with an emphasis on responsiveness and partnership during high-stakes moments

Director of Data Science – Engineering

• Employ advanced statistical methods and machine learning techniques to analyze complex datasets, uncovering deep patterns and root causes
• Architect, build, and maintain robust, end-to-end data pipelines to ensure seamless data collection, integration, and storage
• Design, develop, and deploy predictive algorithms and automated machine learning models into production environments to optimize network operations
• Analyze large, unstructured, and undefined data sets to solve unique technical challenges where information is difficult to obtain
• Lead complex, cross-functional project initiatives to address strategic business needs in cybersecurity, compliance, and network modernization
• Present highly technical findings and strategic recommendations to senior and executive leadership in a clear and actionable manner
• Provide expert guidance, coaching, and mentorship to technical staff across the organization to drive the adoption of data-driven practices
• Conceptualize and implement innovative data products, processes, and standards that establish mid-term operational plans for the broader organization
• Stay updated on the latest advancements in data engineering architecture, machine learning MLOps, and data science best practices

Senior Data Scientist / Machine Learning Engineer

• Lead simple data science and targeting projects
• Build audiences for campaigns by mapping key stakeholders
• Process and analyze data from experiments and surveys
• Produce reports on paid media campaigns
• Enforce existing team processes and identify areas for improvement
• Serve as a bridge between client teams and technical operations

Senior Analyst, Data Science

• Evaluate and assess AI solutions to confirm compliance with governance standards
• Lead initiatives for managing AI/ML controls across diverse teams
• Enhance transparency and reporting processes within AI projects
• Collaborate with stakeholders to develop innovative thought leadership
• Analyze and interpret data to inform governance strategies
• Provide guidance on recommended practices for AI compliance
• Support the implementation of impactful reporting frameworks
• Drive continuous improvement in AI governance practices

AI Engineer, Data Scientist – Senior Associate

• Work remotely in support of a government contract for a client located in Washington, DC.
• Develop and support data-oriented tasks for cybersecurity solutioning.
• Architect and design analytic methods using machine learning.

Data Scientist II

• Work remotely in support of a government contract for a client located in Washington, DC.
• Collaborate on technical solutions and lead implementation efforts.
• Develop and engineer data workflows to merge disparate data sources.
• Design and develop automation for security tools management.
• Apply analytical rigor and statistical methods to analyze large amounts of data.

Data Scientist III

• **Job Description:**
• - Provide guidance and best practices for data collection, storage, analysis and visualization in facility data management.
• - Develop and maintain comprehensive data governance policies, ensuring compliance with regulatory and organizational standards.
• - Verify the implementation of facility management software (e.g., CMMS) to track assets, maintenance schedules, and space utilization.
• - Ensure data integrity by conducting audits, validating information, and implementing data quality control measures.
• - Collaborate with IT and facility teams to integrate data systems and improve reporting capabilities.
• - Analyze facility-related data to identify trends, inefficiencies, and areas for improvement.
• - Generate reports and dashboards to support decision-making and strategic planning.
• - Train facility management teams on data entry protocols, reporting tools, and system best practices.
• - Stay current with industry trends, emerging technologies, and data management advancements.
• - Translate stakeholder requirements into actionable technical solutions using Power BI dashboards and Power Apps
• - Proficiency in Extract, Transform, Load (ETL) processes to ensure efficient data integration and transformation.
• - Implement and oversee data governance strategies to ensure compliance with industry standards and organizational policies.
• - Establish and maintain data quality assurance protocols to ensure reporting accuracy and reliability.
• - Define, configure, update, and track Key Performance Indicators (KPIs) related to organizational requirements and strategic goals.

Facility Data Manager

• Design, build, and maintain scalable data pipelines and workflows to support analytics, fraud detection, model development, and ongoing data monitoring (e.g., using Spark, Airflow, or similar distributed systems).
• Leverage and build agentic AI and LLM-powered systems to automate data exploration, anomaly detection, vendor evaluation, and investigative workflows, increasing the speed and depth of insight generation.
• Build and optimize models using a variety of input data types, including tabular data, natural language, point clouds, and images, in support of fraud detection and identity verification use cases.
• Own data quality and integrity for critical datasets, implementing monitoring, validation checks, and anomaly detection to ensure reliable input to models and downstream decision systems.
• Take ownership of project outcomes from scoping through delivery, managing data quality, technical trade-offs, and timelines; proactively escalate risks and work cross-functionally to resolve challenges.
• Evaluate and integrate third-party data vendors and external datasets, including designing experiments to assess data quality, coverage, lift, and long-term value for Socure’s models and products.
• Collaborate closely with Product, Engineering, and Risk teams to define data requirements, shape roadmap priorities, and deliver insights that guide strategic decisions for fraud and identity products.
• Conduct in-depth research to explore new data sources and develop novel algorithms and features that advance the state of the art in fraud detection, identity resolution, and risk scoring.
• Lead the end-to-end ML/analytics lifecycle for assigned projects: problem definition, data exploration, feature engineering, modeling, evaluation, deployment handoff, and post-deployment monitoring where applicable.
• Present findings, trade-offs, and recommendations to technical and executive stakeholders with clarity and influence, adapting communication for audiences ranging from engineers to non-technical business leaders.
• Mentor and share knowledge with peers and junior data scientists, fostering a culture of experimentation, rapid iteration, and continuous learning aligned to Socure’s leadership competencies.
• Stay current with advancements in AI, machine learning, and data infrastructure (including LLMs and agentic frameworks), and apply innovative techniques to real-world fraud and identity problems.
• Model Socure’s embedded leadership competencies in day-to-day work: continuous learning, effective communication, accountability, team development, decision making, and managing change.

Senior Data Scientist – Fraud Data Infrastructure, Automation

• Lead experimentation and model development for AI/ML solutions in legal products
• Design and evaluate NLP, LLM, and generative AI approaches (e.g., RAG, prompt strategies)
• Define agentic workflows and reasoning strategies for multi-step legal tasks
• Define retrieval strategies, including hybrid search (semantic + lexical), and evaluation metrics (e.g., relevance, ranking quality)
• Analyze large-scale legal datasets to extract insights and improve model performance
• Establish best practices for model evaluation, validation, and benchmarking
• Translate experimental results into clear product recommendations and business impact
• Collaborate with product, legal experts, and engineers to align solutions with user needs
• Mentor team members and provide technical leadership in data science and AI.

Senior Data Scientist – Agentic AI

• Lead advanced measurement strategy across HCP and DTC campaigns, including MMM, multi-touch attribution, incrementality testing, brand affinity, and lift studies.
• Integrate and analyze healthcare datasets including IQVIA, Crossix, PurpleLab, Experian, Medicaid, and other claims or identity-based data sources to quantify media impact.
• Connect exposure across linear TV, Google, SEM, display, paid social, radio, and programmatic to business outcomes and promotional KPIs.
• Query and manipulate large datasets within Snowflake, operating within a medallion (Bronze/Silver/Gold) data architecture.
• Build structured datasets and dashboards in Tableau and Looker that support real-time insight generation and executive reporting.
• Translate statistical and modeling outputs into clear, actionable storylines that demonstrate how campaigns drive business performance.
• Develop executive-ready PowerPoint presentations that bring data to life for clients and senior stakeholders.
• Collaborate closely with Media Strategy, Data Strategy, Engineering, and Account teams to ensure measurement frameworks are aligned with business objectives.
• Mentor analysts and foster a culture of innovation, rigor, and cross-functional partnership within the data science function.

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