Sr. Data Scientist

Overview In this role you will build Python-based data pipelines that ingest data from diverse sources, prepare training and testing sets, and apply machine learning across supervised, unsupervised, and time-series tasks. You’ll analyze raw data with domain experts and ensure data quality with tests and pipelines. You will explore ensemble methods, tune models, and assess leakage to reflect real-world performance, often running large-scale training in the cloud. You’ll share findings with both technical and business teams and mentor colleagues as prototypes mature toward production. Compensation / Benefitsretirement benefits (401k and pension)health and welfare benefits (medical, dental, vision)paid time offparential and caregiver leavelife & accident insurancediscretionary bonus program (potential equity) Responsibilities Develop Python software to acquire and combine data from databases, files, APIs into training/validation/testing datasets Analyze datasets with domain experts to interpret data fields Build unit tests, data quality checks, and data pipelines to ensure trusted data for algorithms Explore and apply algorithms across supervised, unsupervised, and time-series analyses Propose and develop ensemble methods with strong out-of-sample characteristics Tune ML algorithms via hyperparameters and feature selection Detect biases or leakage and ensure realistic train/test splits Run large-scale training/inference on cloud infrastructure (private/public)Present findings using data-science metrics and business language to stakeholders Provide guidance to other software teams as Lab prototypes scale to production Participate across multiple projects throughout the research lifecycle (hypothesis, data acquisition, ETL-style software, findings)Plan and conduct data science training sessions and hackathons Collaborate with external parties (vendors, universities) to adopt new techniques Solve complex problems and offer new perspectives on existing solutions Exercise judgment from multi-source analyses and influence a range of activities within teams Operate within broad guidelines and policies Key requirements Python and common libraries (Num Py, pandas, scikit-learn)Linux-based operating systems and standard development tools Advanced distributed ML frameworks (Keras, Tensor Flow) and Azure cloud infrastructure preferred Strong conceptual and practical knowledge in the field; collaboration across disciplines Ability to explain complex information and build consensus Finance sector experience or related coursework is a pluscross-functional collaborationability to explain technical concepts to non-technical audiencesproblem solving and critical thinking Python Num Pypandas