Swish Analytics

Full-stack Engineer

• Bring your innovation and experience in designing and developing the next-generation data analytics platform using cutting-edge technologies.
• Standardize processes for the complete development cycle including design, implementation, unit testing, code review, testing automation, etc.
• Research and adopt the right technologies to improve the scalability and productivity of the trading analytics group.
• Work closely with key stakeholders and product owners to drive technical design for requirements of various use cases.
• Coordinate with departments (Data, DevOps, Product, Finance, Customer Success, etc.) to ensure a smooth cycle from development to deployment.

Full Stack Engineer

Platform Engineer

• Proficiency in Python for data processing and automation
• Expertise in cloud platforms and their specific data tools
• 24/7 monitoring and incident response of databases
• Developing KPI's, SLA’s, SLO’s
• Backup/recovery testing
• Developing performance tests and query optimizations
• Implementing and maintaining database management strategies
• Creating documentation for company use
• Ensuring database security and compliance

Senior Data Platform Engineer

Analyst

• Monitor live sports markets and market activity in real time across a range of sports and market types
• Support the calibration and refinement of prices using market signals, statistical models, competitor benchmarking, and event-driven information
• Help improve pricing quality through the analysis of market behaviour, price sensitivity, liquidity patterns, and reaction speed to new information
• Contribute to the development, testing, and refinement of quantitative models by applying your understanding of live market dynamics and pricing behaviour
• Own and manage real-time trading risk, including exposure monitoring, liability controls, and disciplined decision-making across concurrent events
• Collaborate with engineering on trading and pricing infrastructure, including API integrations, automated monitoring, alerting, anomaly detection, and execution tooling
• Work closely with Sports Trading teams to interpret breaking news, lineups, injuries, team news, and other event-specific developments to ensure timely and accurate price updates
• Identify model discrepancies, edge cases, and structural inefficiencies in pricing workflows, escalating and documenting findings for Data Science and Data Engineering teams
• Help evaluate market opportunities, prioritise resources across sports and competitions, and improve operational processes as the trading function scales
• Detect sharp or informative market activity and ensure useful signals are fed back into Swish’s proprietary models and pricing systems
• Communicate effectively with internal Sports Trading teams responsible for maintaining and improving our core sportsbook pricing models

Trading Analyst

Data Scientist

• 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.

Data Scientist, NHL

Soccer Data Scientist

Risk

• Own end-to-end research and production pipelines for a strategy
• Lead alpha research initiatives leveraging advanced statistical and machine learning techniques
• Process and analyze high-frequency tick data, order book snapshots, and market microstructure signals with sub-millisecond latency requirements
• Analyze price formation, market liquidity dynamics, and limit order book imbalances across electronic venues
• Build and run Monte Carlo simulations to estimate P&L distributions, risk exposures, and portfolio dynamics
• Develop, backtest, and optimize quantitative trading strategies with rigorous statistical validation
• Interpret complex model outputs and communicate alpha generation mechanisms to portfolio managers
• Write modular, clean, and efficient Python code; build custom analytics libraries and research frameworks
• Lead design reviews and establish data quality and research reproducibility standards
• Guide 1–2 junior researchers through project delivery and model development
• Proactively engage with traders and infrastructure teams to clarify research objectives and resolve data dependencies
• Design and maintain real-time risk monitoring systems across multi-asset portfolios
• Build models for dynamic position sizing, portfolio optimization, and factor exposure management
• Develop stress testing and scenario analysis frameworks for tail-risk events and regime changes
• Collaborate with Trading and Risk Management to define VaR limits, leverage constraints, and implement automated risk controls

Senior Quantitative Researcher – Risk Modeling

Technical Project Manager

• Closely collaborate with cross-functional teams to define, prioritize, and execute on data science & data engineering initiatives, setting strategic direction, ensuring alignment with business objectives, and driving continuous improvement
• Defines and documents project scope, goals, and deliverables that support business goals in collaboration with senior management and stakeholders
• Devising and adapting strategies for executing on necessary goals
• Communicate project status, updates, and key milestones to stakeholders, including senior management.
• Develops full-scale project plans and associated project & communication artifacts
• Managing the data science backlog and prioritizing them based on evolving requirements
• Plans and schedules project timelines and milestones using appropriate tools
• Assist in the staffing and assignment of resources based on project needs

Machine Learning Engineer

• Design, prototype, implement, evaluate, optimize systems to generate sports datasets and predictions with high accuracy and low latency.
• Evaluate internal modeling frameworks and tools to optimize data scientist's modeling workflow.
• Build, test, deploy and maintain production systems.
• Work closely with DevOps and Data Engineering teams to assist with implementation, optimization and scale workloads on Kubernetes using CI/CD, automation tools and scripting languages.
• Support maintenance and optimization of cloud-native EDW and ETL solutions.
• Maintain and promote best practices for software development, including deployment process, documentation, and coding standards.
• Experience applying large scale data processing techniques to develop scalable and innovative sports betting products.
• Use extensive experience to build, test, debug, and deploy production-grade components.
• Participate in development of database structures that fit into the overall architecture of Swish systems.

Basketball Data Scientist

Data Engineer

• Support production systems and help triage issues during live sporting events
• Architect low-latency, real-time analytics systems including raw data collection, feature development and endpoint production
• Build new sports betting data products and predictions offerings
• Integrate large and complex real-time datasets into new consumer and enterprise products
• Develop production-level predictive analytics into enterprise-grade APIs
• Contribute to the design and implementation of new, fully-automated sports data delivery frameworks