Data Scientist

Responsibilities: Perform advanced analytics methods to extract value from business data Conduct large-scale experimentation and build data-driven models to answer key business questions Research cutting‑edge techniques and tools in machine learning, deep learning, and artificial intelligence Determine requirements used to train and evolve deep learning models and algorithms Articulate a vision and roadmap for leveraging data as a valued corporate asset Influence product teams through the presentation of data-based recommendations Evangelize best practices across analytics and product teams Own the entire model development lifecycle — from identifying business requirements and sourcing data to model fitting, presenting results, and production scoring Perform other duties as assigned

The ideal candidate: Master’s Degree in computer science, statistics, economics, or a related field 3+ years of work and/or educational experience in machine learning or cloud computing Experience using statistics and machine learning to solve complex business problems Experience conducting statistical analysis using advanced statistical software Experience with scripting languages and relevant packages Experience building and deploying predictive models Experience with web scraping and scalable data pipelines Experience with big data analysis tools and techniques Up-to-date knowledge of machine learning and data analytics tools and techniques Strong knowledge of predictive modeling methodology Experience leveraging both structured and unstructured data sources Willingness and ability to learn new technologies on the job Demonstrated ability to communicate complex results to technical and non-technical audiences Demonstrated ability to work effectively both independently and in teams across multiple tasks while meeting aggressive timelines Strategic, intellectually curious thinker with a focus on outcomes Professional presence with the ability to build relationships across functions Strong experience with R/RStudio, Python, SAS, SQL, and NoSQL Strong experience with cloud machine learning technologies (e.g., cloud-based ML platforms) Strong experience with machine learning environments (e.g., TensorFlow, scikit-learn, caret) Strong understanding of statistical methods, including Bayesian Networks Inference, linear and non-linear regression, and hierarchical/mixed-models/multi-level modeling Background in financial services preferred Reasons to love itOpportunity to join a well-established organization in the financial services sector High-visibility role with direct influence on senior leadership decision-making Chance to own the full model development lifecycle from concept to production Exposure to cutting‑edge machine learning, deep learning, and AI research and tools Collaborative environment working across analytics and product teams Opportunity to shape the organization’s long‑term data strategy and roadmap