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EXL is a global company providing business process solutions engineered to help companies streamline operations, simplify compliance, prepare for change, and cr
• Design and develop enterprise-wide data governance programs • Define governance operating models and accountability mechanisms • Develop policies and processes for data management • Partner with business and data product teams
• Apply statistical analysis and experimentation to evaluate generative AI, agentic AI, and other AI-enabled applications • Develop and maintain Responsible AI measures, benchmark datasets, test suites, and evaluation criteria • Evaluate AI systems for fairness, bias, explainability, transparency, hallucination, safety, reliability, and robustness • Use evaluation results to support the design, testing, selection, and ongoing assessment of AI-enabled business applications • Establish performance baselines and help identify changes in model or application behavior over time • Design and execute proofs of concept for new Responsible AI evaluation approaches • Assess emerging Responsible AI tools, frameworks, and methodologies for validity, limitations, and business applicability • Support the integration of Responsible AI testing into application development, quality assurance, and governance processes • Partner with engineering, architecture, quality assurance, governance, and business teams to align evaluation methods with use case requirements • Create and maintain model cards, evaluation reports, methodology documentation, and related guidance • Communicate findings, limitations, risks, and recommendations to technical and nontechnical stakeholders • Contribute to Responsible AI training, internal education, applied research, and industry engagement • Monitor emerging technologies and evaluation methods related to generative AI, agentic AI, and Responsible AI.
• Lead the development of models and decision systems that improve how Honor matches caregivers and clients, constructs sustainable schedules, allocates capacity, prioritizes operational work, and responds to changing conditions. • Your team will work directly with Product, Engineering, and Care Operations to turn difficult operating problems into production systems that improve measurable outcomes. • Contribute to Honor’s broader Data Science and Applied AI direction. • Help shape the team’s roadmap and remain hands-on as a player-coach. • Partner with Product, Engineering, and Operations from problem definition through deployment, adoption, and iteration. • Establish strong standards for evaluating, monitoring, and responsibly deploying machine learning and AI-enabled systems. • Lead, coach, and grow the team while remaining hands-on in high-priority projects as a player-coach.
All candidates must meet the following criteria: Must be a US Citizen, no dual Citizenships. Must be able to secure a Public trust clearance. Must be able to work across multiple programs across the Federal and DOD space. The core values that ECS looks for in an engagement manager include: Teamwork, Respect, Accountability, Integrity, and Leadership.
Role Description The Senior Data Solutions Lead is a strategic, hands-on leadership role responsible for driving the design, development, and implementation of enterprise-scale data architecture and solutions. This individual will serve as the primary technical authority for our data engineering initiatives, bridging the gap between complex business requirements and scalable technical deliverables. The ideal candidate will guide a team of data engineers and analysts, champion best practices in data governance, and ensure that our data infrastructure supports advanced analytics, reporting, and mission-critical applications. - Strategic Leadership: Define and execute the overarching data strategy, ensuring alignment with organizational goals and technology roadmaps. - Architecture & Design: Architect scalable, secure, and highly available data platforms, including data lakes, data warehouses, and streaming data pipelines. - Team Management & Mentorship: Lead, mentor, and grow a cross-functional team of data engineers, developers, and analysts to deliver high-quality data solutions. - Stakeholder Collaboration: Partner with cross-functional business leaders, product managers, and external stakeholders to translate complex business needs into robust technical requirements. - Data Governance & Security: Establish and enforce data quality standards, governance frameworks, and security protocols to protect sensitive information and ensure compliance with relevant regulations. - Technology Evaluation: Continuously assess and recommend new data technologies, tools, and methodologies to optimize performance, reduce costs, and improve data accessibility. - Project Delivery: Oversee the end-to-end lifecycle of data projects, ensuring on-time delivery within Agile frameworks. Qualifications - U.S. Citizen: Must be a US Citizen. - Suitability: Ability to obtain and maintain a DHS Public Trust suitability determination. - Experience: 10+ years of progressive experience in data engineering, data architecture, or related fields, with at least 3 years in a leadership or lead architect role. - Cloud Platforms: Deep expertise in one or more major cloud service providers (AWS, Azure, or Google Cloud Platform) and their respective data ecosystems. - Technical Proficiency: Expert-level knowledge of SQL and programming languages such as Python, Scala, or Java. - Data Integration: Proven experience designing and building robust ETL/ELT pipelines using modern integration tools (e.g., Apache Airflow, dbt, Spark, Kafka). - Data Modeling: Strong proficiency in relational and non-relational database design, dimensional modeling, and data warehousing concepts. - Communication: Exceptional verbal and written communication skills, with the ability to explain complex technical concepts to non-technical stakeholders. - Stakeholder Management: Ability to effectively communicate and share knowledge with contacts at all levels. Skilled in developing collaborative relationships. - Team Management: Ability to manage multiple projects and priorities across multiple workstreams with tight deadlines in a fast-paced environment. Requirements - Salary Range: 175,000 - 200,000 Benefits - General Description of Benefits
Role Description We are hiring a Data Scientist to lead forecasting and optimization efforts within our platform. Reporting to the Sr. Director of Engineering, this role will define how quantitative models are built, productionized, and integrated into the system’s core functionality. Our DERMS creates and runs virtual power plants (VPPs), requiring forecasting and optimization of delivered capacity in real-world grid environments. This is a senior individual contributor role with broad ownership. You will design and deploy forecasting and optimization systems that directly shape product behavior and operational workflows. The role requires comfort working across the full stack of quantitative development — from raw data plumbing to production deployment. The candidate must have prior experience in electricity and capacity markets; must be knowledgeable in distribution grid topology; and operational boundaries of batteries, EVs, and large commercial buildings. Qualifications - 7+ years of experience in data science, applied statistics, quantitative engineering, or related fields - Real-world experience building electric load forecasting models - Strong understanding of constraint programming and multi-objective optimization for real-time systems - Experience building optimization models and implementing them in energy / capacity markets - Background in time-series analysis and regression-based modeling - Proven experience deploying and maintaining models in production environments - Comfort working with highly variable real-world data from numerous and varying sources, and building the supporting infrastructure - Strong programming skills in Python (or similar) - Ability to operate autonomously and make sound technical trade-offs - Strong communication skills across technical and non-technical audiences Requirements - Experience in the DERMS / VPP space (Nice to Have) - Familiarity with US energy and capacity markets specifically (Nice to Have) - Background in Bayesian modeling (Nice to Have) - Knowledge of distribution grid topology and the operational boundaries of batteries, EVs, and large commercial buildings (Nice to Have) Benefits - Medical insurance - Vision insurance - Dental insurance - Employer paid life insurance, AD&D, and disability insurance - Competitive 401(k) match – up to 6% - Performance based bonus - Potential profit sharing - Paid time off, holidays and other paid leave
Role Description ClinChoice is searching for a Principal Clinical Data Scientist Consultant – R Programmer to join one of our clients. We are seeking a Principal Clinical Data Scientist to join our Scientific Computing Technology group, which builds the open-source tools, R packages, SAS macros, and computing systems that power clinical programming, data management, and biostatistics across the organization. Reporting to the Director of Statistical Programming, this role blends hands-on clinical deliverables with contributions to the open-source and internal tooling that supports the wider clinical technical community. The ideal candidate has strong R skills, deep clinical programming experience, working Python familiarity, and an interest in modern, reproducible workflows. This position is open to remote candidates. Key Responsibilities - Develop, validate, and maintain SDTM and ADaM datasets in R — using admiral and the broader pharmaverse — following CDISC standards. - Generate Tables, Listings, and Figures (TLFs) in R or SAS as required by study needs. - Perform Pinnacle21 validation, resolve findings, and refine specifications to ensure CDISC compliance and submission readiness. - Write efficient, reproducible, well-structured R code for clinical data analysis and reporting. - Contribute to open-source and internal tooling — including R Shiny / teal modules, R packages, SAS macros, and Python utilities — used by clinical programming, data management, and biostatistics teams. - Partner with statisticians, data managers, and clinical teams to translate programming requirements into reliable deliverables. - Perform QC, reconcile data issues, and ensure outputs meet regulatory expectations (e.g., FDA, EMA). - Support automation, pipeline development, and version-controlled workflows. - Use SAS for legacy studies or where SAS support is needed. Qualifications - Bachelor’s or Master’s degree in Statistics, Computer Science, Mathematics, Life Sciences, or a related field. - 5+ years in clinical programming, with a strong focus on R. - Proven experience producing SDTM and ADaM datasets in R, including hands-on use of admiral. - Experience with Pinnacle21 validation and remediation. - Working knowledge of SAS programming. - Working knowledge of Python for analytics, scripting, or tooling. - Solid understanding of CDISC standards (SDTM, ADaM) and metadata-driven programming. - Experience with clinical trial data, regulatory submissions, and QC processes. - Strong analytical, problem-solving, and documentation skills. Preferred Qualifications - Broader pharmaverse experience (e.g., tidyCDISC, rtables) and tidyverse fluency. - Experience building or contributing to R Shiny apps, teal modules, or R packages used by other teams. - Exposure to AI/ML tooling in a clinical or programming context. - Real-World Evidence (RWE) experience — e.g., mapping Flatiron or claims/EHR data into CDISC-aligned structures. - R Markdown, Quarto, or other reproducible reporting workflows. - GxP validation, Git-based version control, and CI/CD or automated workflows. - CRO or pharmaceutical industry experience. The Application Process Once you have submitted your CV, you will receive an acknowledgement that we have received it. If you have the requirements we need, you will be invited for a phone interview as the first step. Unfortunately, due to the number of applications we receive, we cannot reply to everyone individually if you are not successful.
• Own data management study deliverables end-to-end — from data capture tool development through database lock • Partner closely with CROs and third-party vendors to keep clinical data accurate, timely, and audit-ready • Represent DM as the functional lead at study team meetings; responsible for communication and collaboration • Assumes ownership for DM study deliverables within DM scope of services including overseeing overall quality • Data Reconciliation including coordination of transfers and issue resolution with other functional areas • Investigation and remediation of reviewer-reported data issues • Coordinates with ProKidney, CRO and other 3rd party vendors to develop and maintain DM startup timelines • Responsible for submitting queries in the EDC and with the central laboratory • Ensure quality check of clinical data as appropriate for statistical review, interim review, and final database lock • Responsible for database lock, close-out audit and archiving activities • Manage and store data transfers from 3rd party vendors • Development and management of patient profiles for clinical review and data cleaning
Goldbelt, Inc. is a facilities services company that is “building a brighter future” for its Alaska Native shareholders. The company, as an employer, strive
Role Description The Statistician will provide advanced statistical, analytical, and scientific support to the National Center on Birth Defects and Developmental Disabilities (NCBDDD) at the Centers for Disease Control and Prevention (CDC) surveillance, epidemiologic, and research activities. The Statistician will collaborate with CDC scientists, epidemiologists, clinicians, awardees, and external partners to design, analyze, validate, and disseminate findings from public health research and surveillance projects focused on birth defects, developmental disabilities, maternal and child health, and related conditions. The Statistician will serve as both a lead and validation analyst on complex public health studies, supporting all phases of the research lifecycle, including study design, analytic plan development, data management, statistical programming, data analysis, interpretation of results, scientific dissemination, and peer-reviewed publication. Qualifications - Demonstrated expertise in applying a broad range of statistical and biostatistical methods to public health research, surveillance, and evaluation activities. - Advanced proficiency in SAS and R for data preparation, validation, management, analysis, and automation of reproducible workflows. - Knowledge of epidemiologic principles, surveillance systems, and population-based research methodologies. - Ability to communicate complex statistical concepts and findings to technical and non-technical audiences. - Proven ability to work effectively with multidisciplinary teams. Requirements - Master's degree in Statistics, Biostatistics, Epidemiology, Data Science, Mathematics, Public Health, or a related quantitative discipline. - Minimum five years of experience conducting statistical analyses in public health, epidemiology, healthcare research, or surveillance environments. - Advanced proficiency in SAS and R programming. - Experience managing and analyzing large, complex clinical, surveillance, or population-based datasets. - Demonstrated experience developing statistical analysis plans and conducting multivariable analyses. - Experience supporting scientific publications, technical reports, and research dissemination activities. - Strong written and verbal communication skills. Benefits - The annual salary for this position is $115,000 to $130,000. - Comprehensive benefits package, including medical, dental, and vision insurance. - 401(k) plan with company matching. - Tax-deferred savings options. - Supplementary benefits. - Paid time off. - Professional development opportunities.
An engineering firm that delivers high-quality Healthcare IT, Cybersecurity, and Telecommunication solutions.
• Lead data strategy execution, data supply chain and metadata maturity leadership • Oversee data platform operations, distributed processing, programming and query languages • Operate and improve source onboarding, ingestion, transformation, data quality • Advance the program’s data trust model • Establish and monitor data quality and processing timeliness practices • Coordinate with data owners and teams • Ensure metadata maturity advances as a tracked, multi-year effort • Support governed self-service analytics, certified dashboards, and secure data sharing
An engineering firm that delivers high-quality Healthcare IT, Cybersecurity, and Telecommunication solutions.
• Lead data science work across structured and unstructured program data, including data collection, processing, cleaning, profiling, and preparation for analysis and modeling on a governed data platform. • Design, develop, train, evaluate, and refine machine learning models and AI services, selecting appropriate algorithms and techniques for specific customer and mission needs. • Support deployment, monitoring, and maintenance of model performance in cloud environments using model lifecycle management, model serving, vector search, model evaluation, and related MLOps tooling. • Deliver capabilities across both program AI tracks: internal AI-enabled delivery acceleration (AI-assisted schema tagging, automated code review, documentation generation, ticket automation) and user-facing AI services for customer users and approved consumers (AI assistants, conversational analytics, document-grounded search, and approved retrieval-augmented generation services). • Operate within the program’s AI governance intake and review process, registering all production and pilot AI use cases before deployment, routing managed endpoints through a governed AI gateway layer, and maintaining inference logging, PII guardrails, rate controls, and human oversight and escalation paths. • Plan and conduct proofs of concept and capability-gate evaluations that assess accuracy, governance integration, cost, operational overhead, and alignment to customer policy before scaling new AI capabilities. • Embed responsible AI and equity requirements into delivery, including algorithmic risk and impact assessments, bias testing across relevant demographic and programmatic subgroups, plain-language limitations and escalation paths, and periodic bias and drift re-review. • Collaborate closely with ML engineers, data engineers, platform teams, and cross-functional partners to develop and maintain the infrastructure and governed cloud environment required for AI/ML operations. • Create, maintain, and improve documentation for methodologies, code, assumptions, experiments, evaluation artifacts, and decisions, including documentation of AI tools within the software bill of materials (SBOM), to support reproducibility and governance review. • Communicate technical findings, strategic vision, risks, tradeoffs, and business value to leadership and key stakeholders, and provide people-management support and day-to-day technical guidance to a team of 3–5 engineers.
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Python, SQL, Azure, Cloud, Scala, Java
