Data Scientist

• Lead the design, development, and implementation of advanced machine learning, generative AI solutions across structured and unstructured data
• Build and deploy scalable, automated data science products and reusable analytic assets that support business and client objectives
• Analyze large-scale, high-dimensional datasets using modern analytics tools and distributed computing platforms to identify trends, opportunities, and actionable insights
• Design and prototype agentic AI workflows leveraging large language models (LLMs), retrieval systems, structured data, APIs, tools, and business rules to automate complex business processes
• Translate business requirements into AI agent architectures, including task decomposition, tool orchestration, routing logic, escalation paths, and human approval checkpoints
• Optimize retrieval-augmented generation (RAG) solutions through embedding evaluation, metadata design, reranking approaches, citation quality assessment, and knowledge freshness validation
• Develop evaluation frameworks for machine learning and AI solutions, including performance, accuracy, reliability, hallucination risk, latency, cost, safety, and consistency measures
• Make key analytical and architectural decisions, establish technical standards, and provide leadership on methodology, model selection, solution design, and AI implementation best practices
• Lead code reviews, mentor data scientists, and promote best practices in software development, responsible AI, model governance, and analytics delivery
• Partner with engineering, platform, and business teams to deploy, monitor, and maintain production-ready analytics and AI solutions
• Translate complex analytical findings into clear reports, visualizations, and presentations for technical and non-technical audiences
• Collaborate with stakeholders and clients to define analytical approaches that address strategic, operational, and clinical objectives
• Evaluate emerging technologies and industry trends to advance organizational analytics and AI capabilities.

Lead 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

• Provide expert-level support to Department of Defense (DoD) programs
• Assist government leadership with decision-making, planning, and execution throughout the acquisition life-cycle
• Support configuration management (CM), data management (DM), and quality assurance (QA) activities
• Manage configurations of software, engineering documents, design drawings, engineering changes, and audits
• Develop and administer Configuration Management Plans and maintain CM files, status accounting records, and baselines
• Convene and manage Configuration Control Boards (CCBs)
• Track and manage Change Requests (CRs), Discrepancy Reports (DRs), and Problem Reports (PRs)
• Conduct and support audits including Functional and Physical Configuration Audits (FCAs/PCAs)
• Maintain the CM library and software repositories
• Support data calls, review CDRLs, monitor contractor compliance, and manage data item deliveries
• Ensure configuration control for IT systems

Configuration and CDRL Data Manager

• Develop, implement, and deploy machine learning models and statistical analyses to optimize energy storage and solar asset performance across Stem's portfolio
• Design and build data pipelines to extract, transform, and analyze large-scale datasets from energy systems
• Collaborate with software engineering, product management, and energy experts to integrate data-driven solutions into PowerTrack platforms
• Create data visualizations, dashboards, and reports to communicate insights and recommendations to stakeholders
• Conduct exploratory data analysis to identify trends, patterns, and opportunities for improving system performance and business outcomes

Data Scientist, Forecasting – Energy Storage System Optimization

• Performs analytical tasks on structured and unstructured data to extract actionable business insights.
• Participates in the data gathering, data processing and data mining of large and complex datasets.
• Develops algorithms using advanced mathematical and statistical techniques like machine learning to predict business outcomes and recommend optimal actions to management.
• Runs analytical experiments in a methodical manner to find opportunities for product and process optimization.
• Assists in the presentation of business insights to management using visualization technologies and data storytelling.
• May partner with Data Architects, Data Analysts, Data Engineers and Visualization Experts to develop data-driven solutions for the business.

Data Scientist, Gen AI Governance

• Provides financial and regulatory reporting and analyses to maintain adequate controls over the financial and regulatory reporting processes
• Responsible for running complex business performance, risk and operational analytics
• Develops analytical methods/models to assess market, credit and/or operational risk of new and existing financial products
• Analyzes large datasets to improve risk adjusted returns, deliver profitable growth, and communicate conclusions
• Synthesizes analytical results to develop strategies that improve lending decisions and manage risk
• Applies predictive models and other tools to develop and execute appropriate segmentation and targeting for acquisition
• Works with business, credit, data, and model development partners to design and monitor test designs and analytical reporting

Fraud Data Scientist – Card Transactions

• Design reusable and AI-ready data products aligned to the organizational structure of retail-banking portfolios and associated process families
• Define and lead the vision, strategy, and roadmap for reusable, AI-ready retail banking data products
• Align data product investments to enterprise priorities including personalization, automation, responsible AI, and digital transformation
• Translate business capabilities into modular data products (datasets, features, semantic layers, APIs) designed for repeated use across channels and use cases
• Manages all aspects of organizational data; establishes and maintains policies and procedures for planning, designing and maintaining an information bank to ensure that data services are delivered in a high-quality and profitable manner
• Provides senior leadership, direction and methodology for analysis, structure and storage of critical organizational data
• Oversees the integrity and maintenance of organizational data
• Monitors and reviews major organizational activities and goals to ensure important data needs are anticipated, and met
• Reviews and recommends policies and procedures for collecting and managing organization data.

Senior Data Manager – Retail Data Product Design

• Develop, analyze, evaluate, and deliver actionable analytics that inform key supply chain decisions, optimize performance, and support the successful execution of strategic initiatives
• Lead the development, optimization, and continuous improvement of databases, data pipelines, and analytics infrastructure to ensure data integrity, accessibility, and performance
• Own the design of Power BI dashboard to support the business needs and the ability to build tabular and/or visualization reports as needed
• Develop, implement, and maintain databases, data collection systems, data analytics and other strategies that optimize statistical efficiency and quality with data from primary or secondary data sources.
• Lead the identification, analysis, and interpretation of trends or patterns in complex data sets using statistical techniques
• Perform extensive data validation/quality assurance analysis within large datasets
• Share compelling, insightful storytelling so the business results/needs are understood

Supply Chain Analytics Lead

• Define and drive a clear analytics vision aligned to client engagement, media performance, and marketing measurement priorities.
• Champion a data-driven culture by embedding analytics into strategic planning, campaign optimization, and decision-making processes.
• Lead the development and execution of measurement frameworks tailored to each client’s objectives, ensuring alignment with business goals.
• Translate data into actionable insights by identifying performance drivers, resolving anomalies, and crafting compelling narratives that demonstrate media impact.
• Establish and maintain robust testing frameworks that are statistically valid and operationally scalable.
• Drive confidence and alignment by delivering actionable insights rooted in analytics, business strategy, and persuasive communication.
• Provide business feedback as a key stakeholder in the development of standardized dashboards and advanced analytics to measure the effectiveness of campaigns.
• Leverage predictive and prescriptive analytics to identify trends, opportunities, and risks related to client behavior and retention.
• Utilize advanced analytics to quantify marketing impact and ROI, and partner cross-functionally to diagnose and address performance irregularities.
• Implement robust A/B and multivariate testing and experimentation protocols to optimize campaigns and maximize impact.

Senior Manager, Data Science & Analytics

• 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

• Lead the design, development, deployment, and optimization of machine learning, predictive analytics, and AI-powered solutions.
• Translate business challenges and opportunities into analytical approaches, model specifications, and measurable success criteria.
• Apply advanced statistical analysis, machine learning techniques, and data science methodologies to solve complex business problems.
• Analyze large, complex datasets to identify trends, patterns, opportunities, and actionable insights.
• Develop and maintain model documentation, technical specifications, and implementation plans.
• Stay current with emerging technologies, tools, and best practices in data science, machine learning, and artificial intelligence.
• Design and execute comprehensive validation and evaluation strategies for machine learning and generative AI solutions.
• Develop benchmarking frameworks and success metrics to assess model performance, reliability, and business impact.
• Evaluate model quality using quantitative and qualitative measures, including accuracy, precision, recall, robustness, latency, and business outcome metrics.
• Assess generative AI applications for response quality, grounding, relevance, consistency, and hallucination risk.
• Identify and mitigate risks related to bias, fairness, explainability, privacy, and model reliability.
• Perform model validation, testing, and performance assessments prior to production deployment.
• Establish monitoring processes and evaluation methodologies to ensure continued model effectiveness and alignment with business objectives.
• Design, execute, and analyze experiments, including A/B tests and statistical studies, to measure product and business outcomes.
• Define key performance indicators and success metrics for machine learning and AI initiatives.
• Measure and communicate the impact of analytical solutions through statistical analysis and quantitative methods.
• Partner with stakeholders to define hypotheses, success criteria, and decision-making frameworks.
• Use experimentation and data-driven insights to guide product, operational, and strategic decisions.
• Collaborate with Engineering and Data Engineering teams to implement, operationalize, and scale models in production environments.
• Monitor deployed models for performance degradation, model drift, data quality issues, and changing business conditions.
• Recommend retraining, optimization, or replacement strategies based on model performance and evolving business needs.
• Support the creation of scalable, maintainable, and reliable AI and machine learning solutions.
• Ensure model deployment processes align with engineering best practices and operational requirements.
• Partner with Product, Engineering, Analytics, and business stakeholders to prioritize opportunities and deliver high-impact solutions.
• Communicate complex analytical findings and technical concepts to both technical and non-technical audiences.
• Present recommendations, insights, and model performance results to leadership and project teams.
• Support technical reviews, project planning, and delivery activities across cross-functional initiatives.
• Contribute to knowledge sharing, documentation, and best practices within the data science organization.
• Provide technical guidance and mentorship to junior team members and peers as needed.

Senior Data Scientist

• Translate complex business challenges into practical, data-driven solutions using statistical analysis, machine learning, simulation, and optimization techniques.
• Design and deliver scalable solutions that support internal decision-making and power customer-facing applications.
• Leverage expertise in databases, cloud platforms, and programming to develop advanced analytical models.
• Collaborate with peers to enhance internal tools and expand the organization’s library of data science capabilities.
• Gathers, interprets, and manipulates structured and unstructured data to enable advanced analytical solutions for the business.
• Develop scalable, automated solutions using machine learning, simulation, and optimization to deliver business insights and business value.
• Select the appropriate modeling technique and/or technology considering data limitations, application, and business needs.
• Develops and deploys models within the Model Development Control (MDC) and Model Risk Management (MRM) framework.
• Communicate outcomes to non-technical business colleagues with focus on business action and recommendations.

Data Scientist I

• 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

• Design, develop, and deploy advanced AI/ML models and algorithms that improve operational efficiency, forecasting accuracy, and decision intelligence across the Hunter Douglas supply chain.
• Conduct data exploration, feature engineering, and model tuning to ensure accuracy, robustness, and business applicability.
• Collaborate with business stakeholders to translate complex challenges into data-driven solutions aligned with organizational goals.
• Monitor, retrain, and optimize models post-deployment to maintain high performance as data and business conditions evolve.
• Apply state-of-the-art methods such as neural networks, reinforcement learning, natural language processing (NLP), and generative AI to tackle diverse business challenges.
• Contribute to the AI strategy roadmap, ensuring alignment with Hunter Douglas’s digital transformation initiatives.
• Work with data engineers and IT to ensure efficient data pipelines, scalable infrastructure, and model deployment.
• Partner with internal and external technical teams, vendors, and consultants to implement and scale AI solutions.
• Communicate findings and insights clearly to both technical and non-technical stakeholders through visualizations, presentations, and storytelling.
• Stay at the forefront of AI and analytics innovation, evaluating new tools, methodologies, and technologies for potential adoption.
• Collaborate with digital product and IT teams to develop and integrate AI-assisted tools and automations within Hunter Douglas’s VIBE platform and data ecosystem.
• Experiment with AI coding assistants (e.g., Cursor, GitHub Copilot, OpenAI Codex) to accelerate analytics and model development and improve reproducibility.
• Support the creation of custom AI copilots and prompt libraries for use in digital supply chain tools, analytics dashboards, and planning applications.
• Partner with the Digital Supply Chain Leader to prototype intelligent process automation for recurring planning, logistics, quality, or operational workflows.

AI Data Scientist

• Advising and providing mentorship and guidance to junior data analysts.
• Advocating for best-in-class processes and techniques for statistical analysis, modeling, and Big Data capabilities.
• Fostering effective working relationships and data-fueled solutions that drive the business toward its goals.
• Ensuring statistical integrity, accuracy, and adequacy within the department.
• Innovating, exploring, and practicing the art of ‘the possible’ to uncover valuable data science opportunities and bring them to fruition.
• Conducting research, designing, implementing, and deploying scalable data analytics and machine learning solutions.
• Dissecting complex problems and finding data-driven solutions; addressing various business issues by leveraging diverse data sources.
• Developing and maintaining an analytics infrastructure for widespread use.
• Delivering advance AI and GenAI models to enable desired business outcomes.
• Performing development at all stages in the data science lifecycle; working autonomously and taking ownership of projects and driving them forward.
• Clearly communicating findings to technical and non-technical enterprise stakeholders in addition to various levels of organizational leadership.

Principal Data Scientist

• Lead cross-functional Data Science projects from initiation to implementation and operational transition utilizing various methodologies.
• Develop project artifacts including charters, schedules, budgets, status reports, and risk registers.
• Drive accountability across technical teams, vendors, and business stakeholders.
• Serve as primary liaison between business owners, Data Science teams, and vendors.
• Facilitate project meetings, executive updates, and decision-making forums.
• Communicate project status, risks, and mitigation plans to leadership and executives.
• Proactively manage risks, issues, dependencies, and change requests.
• Ensure project objectives align with business outcomes and enterprise technology strategy.
• Partner with enterprise architects, infrastructure, and data science teams to ensure compliance with standards.
• Translate technical concepts into business-focused communication for executives and non-technical stakeholders.
• Oversee vendor deliverables, SOWs, and integration activities.

Technical Project Manager, Data Science

• Lead cross-functional Data Science projects from initiation through implementation and operational transition.
• Develop project artifacts including charters, schedules, budgets, status reports, and risk registers.
• Drive accountability across technical teams, vendors, and business stakeholders.
• Serve as primary liaison between business owners, Data Science teams, and vendors.
• Facilitate project meetings, executive updates, and decision-making forums.
• Communicate project status, risks, and mitigation plans to leadership and executives.
• Manage risks, issues, dependencies, and change requests proactively.
• Ensure project objectives align with business outcomes and enterprise technology strategy.
• Partner with enterprise architects, infrastructure, and Data Science teams to ensure compliance with standards.
• Translate technical concepts into business-focused communication for executives and non-technical stakeholders.
• Oversee vendor deliverables, statements of work, and integration activities.

Senior Technical Project Manager, Data Science

• Gather, preprocess, and validate datasets from internal and external sources.
• Identify trends, patterns, and anomalies using statistical and visual techniques.
• Build and evaluate predictive models using machine learning algorithms.
• Create dashboards and visual reports to communicate findings to stakeholders.
• Maintain clear records of methodologies, code, and project outcomes.
• Work closely with cross-functional teams to align data solutions with business needs.

Data Science, AI Intern

• 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

• Own the proprietary model flywheel with 90M+ assigned programs and build a proprietary therapeutic AI model
• Lead AI product delivery across all AI bets and drive design, development, deployment, and continuous improvement of AI systems
• Define and execute the AI/ML technical roadmap by collaborating with Product and Content to prioritize clinical and product needs
• Establish trust-by-design as a standard by ensuring transparency and auditability in all AI features
• Own the clinical AI safety and quality framework, ensuring HIPAA-compliant data handling alongside Legal
• Drive cross-functional alignment and work closely with various teams to embed AI in workflows
• Continuously evaluate emerging developments in LLMs and clinical AI for practical advantages

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