• Lead and manage a team of senior associates and associates within the Banking/Investing vertical, driving team performance, productivity, and adherence to KPIs, identifying training needs and support capability building within the team • While performing research, oversee end-to-end project execution within the Banking/Investing domain • Ensure timely delivery of high-quality research • Develop and share subject knowledge expertise with team members, to help them navigate complex projects and act as a Subject Matter Expert (SME) for domain-specific queries • Support onboarding and training new team members, conduct knowledge-sharing sessions on Banking/Investing metrics • Develop and maintain SOPs, guidelines, and knowledge of repositories • Collaborate with analysts, to understand requirements and align on deliverables • Provide regular updates on progress, challenges, and key insights • Track and evaluate team performance metrics, provide regular feedback to members in areas to improve, ensure timely delivery of projects with high accuracy (target: ≥97%) • Proactively manage escalations and resolve delivery challenges • Drive continuous improvement initiatives within the team, identify opportunities to improve workflows, and optimize processes

Role Description Swish Analytics is looking for a Soccer Data Scientist to join our ever-growing team! Data Science is at the core of our business, so this team has true ownership and impact over developing core components of Swish's data products. This position is remote from the USA. - Ideate, develop and improve machine learning and statistical models that drive Swish’s core algorithms for producing state-of-the-art sports betting products. - Develop contextualized feature sets using sports specific domain knowledge. - Contribute to all stages of model development, from creating proof-of-concepts and beta testing, to partnering with data engineering and product teams to deploy new models. - Strive to constantly improve model performance using insights from rigorous offline and online experimentation. - Analyze results and outputs to assess model performance and identify model weaknesses for directing development efforts. - Adhere to software engineering best practices and contribute to shared code repositories. - Document modeling work and present to stakeholders and other technical and non-technical partners. Qualifications - Masters degree in Data Analytics, Data Science, Computer Science or related technical subject area. - Demonstrated experience developing models at production scale for Soccer, or sports betting for 2+ years. - Expertise in Probability Theory, Machine Learning, Inferential Statistics, Bayesian Statistics, Markov Chain Monte Carlo methods. - 5+ years of demonstrated experience developing and delivering effective machine learning and/or statistical models to serve business needs in sports or sports betting. - Experience with relational SQL & Python. - Experience with source control tools such as GitHub and related CI/CD processes. - Experience working in AWS environments. - Proven track record of strong leadership skills. Has shown ability to partner with teams in solving complex problems by taking a broad perspective to identify innovative solutions. - Excellent communication skills to both technical and non-technical audiences. Requirements - Base Salary: Starting at $130,000. Company Description Swish Analytics is a sports analytics, betting and fantasy startup building the next generation of predictive sports analytics data products. We believe that oddsmaking is a challenge rooted in engineering, mathematics, and sports betting expertise; not intuition. We're looking for team-oriented individuals with an authentic passion for accurate and predictive real-time data who can execute in a fast-paced, creative, and continually-evolving environment without sacrificing technical excellence. Our challenges are unique, so we hope you are comfortable in uncharted territory and passionate about building systems to support products across a variety of industries and consumer/enterprise clients.

• Serve as the global owner for the commercial customer data pillar, with a core focus on accounts and contacts across commercial platforms and systems. • Design, manage, and maintain core customer data views that support business needs across multiple commercial systems. • Lead the structure and ongoing maintenance of the Account and Contact 360 views in partnership with business unit stakeholders and cross functional partners. • Own and maintain the Commercial MDM data structure globally, ensuring account, contact, and related sales data are represented in a streamlined, meaningful, and scalable way. • Drive simplification across Orthopedics commercial data while preserving the functionality required by business users and commercial teams. • Partner with business and technical stakeholders to ensure customer data is visualized and reported with quality, accuracy, and clarity. • Reduce ambiguity and confusion in customer data by establishing clear ownership, definitions, governance, and reporting standards. • Support product governance activities and maintain partnership with the Data Strategies team to ensure alignment across related data and platform initiatives.

• Coach and Develop the Team • Own the Roadmap • Build Cross-Functional Relationships • Drive Marketplace Forecasting • Own Production ML Systems • Establish Best Practices