VERSANT (Nasdaq: VSNT) is an industry-changing media and entertainment business and home to trusted brands that shape culture, inform audiences, and build lasting connections. It operates across four core markets: political news and opinion, business news and personal finance, golf, and sports and genre entertainment. These markets are served through a powerful portfolio of iconic and innovative brands, including CNBC, MS NOW, USA Network, Golf Channel, Oxygen, E!, SYFY, and Versantâs sports division USA Sports, along with complementary digital assets including Fandango, Rotten Tomatoes, GolfNow and GolfPass.
This Senior Data Analyst role sits within the AI, Modeling, and Strategy team (AIMS), part of the broader Data Product and Technology team. The position is responsible for supporting the ongoing operations, enhancement, and maintenance of data products.
In this role, the senior data analyst will contribute to expanding the organization’s analytical and AI capabilities within a fast-paced, innovative environment, playing a key part in developing scalable data products that deliver measurable value to both internal stakeholders and external business partners.
Responsibilities:
- Design, develop, and implement advanced algorithms and automated analytical pipelines, including clustering (e.g k-means, hierarchical clustering, DBSCAN, HDBSCAN), supervised learning (e.g. linear and logistic regression, decision trees, random forests, gradient boosting, XGBoost), time-series forecasting (e.g. ARIMA, Prophet, LSTM), optimization techniques, and probabilistic models to analyze large, complex, and unstructured datasets.
- Develop, refine, validate and deploy scalable advanced analytical and statistical models to generate actionable insights for key stakeholders
- Partner closely with finance, product, pricing, marketing, and data services teams to translate business questions into analytical solutions, productionize models using MLOps best practices, and support the ongoing monitoring, maintenance, and optimization of data products and models.
- Cleanse, integrate, and validate data from multiple internal and external sources, applying data quality controls, feature engineering techniques, and scalable transformation processes to ensure accuracy, reliability, and analytical readiness.
- Evaluate and monitor model performance using appropriate metrics, diagnose anomalies and model drift, conduct A/B testing and experimentation, and define, monitor, and optimize KPIs supporting revenue, pricing, and growth initiatives.
- Leverage modern AI