• Leading the use of data to make decisions with a focus on supporting the Casualty business through predictive modeling and machine learning solutions. • Developing and recommending new machine learning predictive modeling algorithms. • Coding/developing tools that use machine learning/predictive modeling to make business decisions. • Searching for and integrating new data to improve modeling and machine learning results. • Discovery of new business problems that can be solved through the use of machine learning/predictive modeling. • Mentoring and developing junior data scientists and managing complex projects as an individual contributor.

• Support Army logistics enterprise data management through data governance, data quality, metadata management, data modeling, and strategic initiatives. • Conduct research and analysis to evaluate enterprise data, business processes, data quality, and emerging issues affecting Army logistics operations. • Develop and maintain enterprise data models, business rules, metadata, and documentation supporting Army logistics information requirements. • Support data governance activities by implementing standards, policies, procedures, and best practices that improve enterprise data management. • Analyze, validate, and monitor data quality to identify inconsistencies, recommend corrective actions, and improve data integrity across enterprise systems. • Collect, consolidate, validate, and analyze information from multiple enterprise data sources to support executive decision making and organizational priorities. • Develop executive-ready briefings, reports, decision papers, presentations, dashboards, and analytical products supporting Army logistics leadership. • Coordinate activities and recommendations across multiple Army organizations and stakeholders to improve enterprise data management and governance. • Facilitate meetings, working groups, and collaborative planning sessions while documenting decisions and tracking follow-up actions. • Support enterprise data stewardship, information management, reporting, and modernization initiatives that improve organizational performance.

Role Description We are looking for a founding Senior Data Scientist to help build high-impact decision systems in a fast-paced, startup-style environment within a large organization. This is a hands-on builder role for candidates who thrive in ambiguity, move quickly from idea to production, and are energized by turning complex business problems into scalable data products. You will work closely with Staff and Principal Data Scientists to deliver production-grade systems across: - Forecasting - Experimentation - Constrained optimization - Pricing - Batch and real-time recommendation systems The role requires strong end-to-end ownership from problem framing and modeling through production deployment using self-service ML platform tooling. This role is based out of Toronto. Key Responsibilities - Design, build, and deploy end-to-end decision science systems spanning demand forecasting, experimentation, portfolio optimization, pricing, and recommendation systems. - Build batch and real-time recommendation pipelines using multi-stage cascading ranking architecture, including candidate generation, pre-ranking, ranking, and re-ranking. - Translate ambiguous business problems into structured hypotheses, measurable KPIs, experimentation plans, and production solutions. - Partner closely with MLEs to leverage self-service deployment tooling, observability, shadow deployment, canary rollout, and KPI monitoring workflows. - Own one or more domain problem areas end-to-end, driving measurable business impact through fast iteration cycles. - Contribute to modeling standards, code quality, validation rigor, and experimentation best practices established by Staff and Principal DS leadership. - Mentor junior Data Scientists and contribute to the technical growth of the founding team. Qualifications - Master’s degree or PhD in Computer Science, Statistics, Mathematics, Engineering, Operations Research, Economics, or another related STEM field. - 2+ years of hands-on experience in data science, decision science, econometrics, or applied machine learning. - Proven ability to independently deliver end-to-end data science systems from problem framing through measurable production impact. - Demonstrated experience in at least two of: forecasting, experimentation, optimization, recommendation systems, pricing, causal inference, or portfolio science. - Comfortable operating in fast-paced, startup-style environments with evolving priorities and high ownership expectations. Requirements - Strong Python proficiency across pandas, scikit-learn, PyTorch, and TensorFlow. - Strong SQL and large-scale data manipulation experience. - Solid grounding in statistical modeling, machine learning, experimentation, and optimization. - Hands-on experience building production-grade batch and low-latency real-time decision systems. - Familiarity with multi-stage ranking systems, ANN retrieval, embeddings, and vector search is strongly preferred. Soft Skills - Strong communication skills with ability to present complex findings to business and technical stakeholders. - Collaborative mindset with ability to work cross-functionally with DS, MLE, and product teams. - Strong execution bias and comfort with rapid iteration under ambiguity. Preferred Qualifications - Experience as an early or founding Data Scientist in a new team or product area. - Hands-on portfolio optimization, assortment optimization, payout optimization, or mathematical programming. - Experience with personalization, gaming, retail, marketplace, or digital consumer decision systems. - Familiarity with Databricks, PySpark, MLflow, experimentation tooling, and cloud-native deployment workflows. - Strong product intuition for balancing revenue, engagement, margin, and responsible use constraints. Company Description Scientific Games is the global leader in lottery games, sports betting and technology, and the partner of choice for government lotteries. From cutting-edge backend systems to exciting entertainment experiences and trailblazing retail and digital solutions, we elevate play every day. We push game designs to the next level and are pioneers in data analytics and iLottery. Built on a foundation of trusted partnerships, Scientific Games combines relentless innovation, legendary performance, and unwavering security to responsibly propel the global lottery industry ever forward.

Role Description As a Data Scientist at Gainwell, you will support Gainwell's Medicaid and public sector analytics initiatives by leading advanced data science activities across complex healthcare and enterprise datasets. The role focuses on applying statistical modeling, machine learning, and scalable analytics techniques to generate actionable insights that inform business, clinical, and programmatic decisions. You will function independently within a business area while collaborating across multidisciplinary teams. At this level, the Data Scientist is expected to influence analytical approaches, mentor junior staff, and contribute to the continuous improvement of Gainwell's data science practices, tooling, and delivery standards. This role is strictly involved in data analytics, modeling, and advanced data science solution development and does not involve access to Protected Health Information (PHI), Personally Identifiable Information (PII), or any secured or confidential client data. The work is limited to analytics development, model design, and insight generation using approved and governed datasets and does not include handling or processing of sensitive health or personal information. Your role in our mission: - Lead data ingestion, cleansing, transformation, and aggregation efforts for large scale and complex datasets. - Design and implement advanced feature engineering, statistical estimation, and hypothesis testing techniques. - Develop, validate, and refine machine learning and statistical models, including time series, repeated measures, and mixed effects models. - Ensure analytical rigor by addressing overfitting, false discovery, bias, and model generalizability. - Analyze healthcare and enterprise datasets to surface complex, high impact, actionable insights that support strategic decision making. - Drive iterative model development and support continuous integration and deployment of analytics solutions. - Optimize data science solutions for performance, scalability, and production readiness. - Leverage cloud based platforms to support elastic, high volume data science workloads. - Collaborate with business stakeholders, data engineers, architects, and analysts to align analytics outputs with business objectives. - Provide technical leadership and guidance to junior data scientists and analysts. - Contribute to the definition and evolution of data science standards, best practices, and reusable analytics assets. - Clearly document analytical methodologies, assumptions, results, and recommendations. - Present insights and recommendations effectively to technical and non-technical stakeholders, including leadership audiences. Qualifications - 10+ years of experience in data science, advanced analytics, or related roles. - Expert proficiency in SQL, including complex set-based query development for large scale datasets. - Deep, hands-on experience with SQL windowing functions. - Strong understanding of database concepts such as indexing, stored procedures, and materialized views. - Advanced proficiency in Python, including object-oriented design and common machine learning libraries. - Strong knowledge of statistical methods, including time series analysis, repeated measures, mixed effects models, and hypothesis testing. - Proven experience applying machine learning techniques, including model evaluation, tuning, and lifecycle management. - Experience with Dev/Sec/Ops practices and CI/CD pipelines for analytics development and deployment. - Strong experience in performance optimization for both development and production analytics environments. - Hands-on experience using Databricks for enterprise data science workloads; Scala knowledge is a plus. - Knowledge of semi-structured and unstructured data, schema on read techniques, parsers, and NLP libraries. - Demonstrated experience deriving insights from healthcare datasets. - Experience performing data science in a major cloud environment (AWS, Azure, or GCP). Benefits - Remote working - Work life balance - Shift timing: 1pm to 10pm