• Own the end-to-end metric work in emerging product and customer experience areas. • Deliver effective exploratory analyses and advanced methods. • Collaborate with cross-functional teams to develop strategies. • Create effective dashboards and analytical artifacts. • Navigate an enterprise-scale data environment. • Proactively identify opportunities to improve standards.

• Develop and maintain sports simulation models to accurately price a wide range of sports betting markets • Contribute to the creation and improvement of algorithms for odds compilation and risk management across various sports and bet types • Work with data engineering teams to ensure efficient processing and utilization of sports datasets • Assist in implementing and improving models for live betting, taking into account real-time data and market movements • Conduct analysis of historical datasets to inform pricing strategies and identify potential risks • Collaborate with trading teams to provide quantitative insights and support for decision-making • Stay informed about the latest developments in sports and betting technologies, proposing new methodologies as appropriate

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description The Principal Data Analyst is a senior, hands-on analytics leader who turns complex, cross‑functional data into clear, actionable insights that drive business outcomes as a consultative partner. This role sets the bar for analytical rigor, designs enterprise‑grade BI assets, mentors analysts, and partners with business, engineering, and governance to improve decision quality and operational performance. Work emphasizes advanced analytics using Databricks, SQL, R, Power BI, Python, and the Azure data stack. Salary range: $100,003-130,003 plus 10% bonus At this time, we are NOT considering applicants that require immigration sponsorship (additional work authorization or permanent work authorization) now or in the future to work in the United States. This includes, but IS NOT LIMITED TO: F1-OPT, F1-CPT, H-1B, TN, L-1, J-1, etc. Responsibilities - Lead high‑impact analytics initiatives from problem framing through delivery; quantify value, design robust analyses, and communicate recommendations to business partners and executives. - Partner with business leaders to identify analytics opportunities and deliver actionable insights. Present findings and recommendations to partners and executive leadership in clear, compelling formats. - Develop analytics products and solutions using Databricks, modern Business and Artificial Intelligence tools and Agile principles. - Implement advanced analytics where applicable, leveraging Python for advanced analytics, automation, and integration with Databricks workflows. - Architect and publish trusted Power BI datasets and dashboards (DAX, Power Query, semantic models), establishing standards for usability and adoption. - Build performant Databricks workflows (notebooks, jobs, SQL Warehouses) to wrangle large datasets, engineer features, and automate recurring analyses. - Develop Python scripts for automation, data wrangling, and integration with Databricks and Azure services. - Partner with Data Engineering on Azure data stack components (Data Factory, ADLS, Synapse/Fabric pipelines) for scalable data solutions. - Own analysis quality: data validation, experiment/study design, sensitivity checks, and reproducibility (versioning, documentation). - Define and monitor KPIs; create executive scorecards and operational reporting that tie directly to business objectives. - Coach/mentor analysts and BI developers; uplift storytelling, statistical thinking, and visualization craftsmanship across the team. - Collaborate with Data Governance and Compliance to ensure appropriate use of PHI and adherence to privacy/security controls. - Facilitate data stewardship and literacy across the business. - Drive adoption of data products to scale data value. - Stay current with emerging technologies and recommend enhancements to the analytics stack. Qualifications - Bachelor’s degree in Data Science, Computer Science, Statistics, Economics or related field. - 8–10+ years in analytics roles, including 3+ years leading projects. - Proven impact delivering analytics in complex environments (multi‑source data, ambiguous scope) with measurable business outcomes. - Experience operating in healthcare data environments (or demonstrated ability to quickly adapt to such contexts). - Demonstrated ability to mentor junior analytics professionals. - Statistical and financial analytics expertise with deep understanding of appropriate study design and analysis techniques and business case development. - Azure Data Stack: Data Factory, ADLS, Synapse/Fabric pipelines; familiarity with security and governance features. - Databricks: Spark/SQL, notebooks & jobs, performance tuning, SQL Warehouses. - SQL: Strong proficiency (ANSI/T‑SQL); query optimization over large, partitioned tables. - R: Applied analytics using tidyverse/ggplot2; reproducible workflows; statistical testing and modeling fundamentals. - Python: Data wrangling, automation, integration with Databricks and Azure services. - Fabric and Power BI: Data modeling, DAX, Power Query (M), calculation groups, row‑level security, deployment pipelines. - Versioning & Reproducibility: Git‑based workflows; disciplined documentation and peer review. - Executive storytelling: distill and translate complex analyses into clear narratives and recommended actions or analytics solutions. - Stakeholder partnership: influence cross‑functional partners; set expectations and drive alignment. - Demonstrated ability to deliver projects with undefined, large and/or complex scope. - Agile delivery. - Mentorship: develop analysts’ craft (methodology, viz/design, communication). - Product mindset: define problem statements, success metrics, and iterate with feedback – drives adoption of analytics products and solutions. - Ownership & judgment: operate independently, make sound trade‑offs, and uphold high quality standards. Preferred Skills and Experience - Master’s degree in Economics, Statistics, Data Science, Computer Science or related field. - Certifications in Databricks, Power BI, or cloud platforms (Azure preferred). - Experience with machine learning frameworks and MLOps practices. - Familiarity with data governance tools and compliance standards. - Knowledge of advanced BI features (Power BI Copilot, AI-driven analytics). - Familiarity with healthcare risk adjustment models such as HCC and CDPS. - Familiarity with claims groupers such as Milliman’s HCG. - Product management training or analytics product delivery experience. Benefits - PTO, holiday pay and holiday of choice. - 401(k) match. - Life insurance. - Short-term disability. - Health, dental and vision insurance. - Maternity/paternity leave. - Health savings account (HSA). - Flex spending accounts (FSA) – health and dependent. Physical Requirements - Repetitive motions that include the wrists, hands and/or fingers. - Sedentary work that primarily involves sitting, remaining in a stationary position for prolonged periods. - Visual perception to perform job including peripheral vision, depth perception, and the ability to adjust focus.

• Develop, maintain, and optimize Power BI reports and dashboards to provide actionable insights to aircraft operators and other stakeholders. • Collaborate with aircraft operators to understand their data needs and provide tailored business intelligence solutions that support their daily operations. • Conduct data analysis and data mining to identify trends, patterns, and opportunities for improvement within aviation operations. • Work closely with cross-functional teams to ensure data accuracy, integrity, and availability for reporting and analysis. • Support the design and implementation of data models and data architecture to streamline data flow and enhance reporting capabilities. • Monitor and manage data quality, performing regular audits to ensure the reliability of business intelligence outputs. • Provide training and support to users on Power BI and other data tools to promote self-service analytics and data-driven decision-making. • Stay updated with the latest advancements in data analytics and Power BI to continuously improve our reporting and analytical capabilities.