Sr. Data Scientist

Experteer Overview In this Data Scientist role, you will develop and maintain ML pipelines and software to acquire, prepare, and analyze diverse data sources. You will work with domain experts to understand data context and evaluate models, while ensuring data quality and robust testing. You will train and tune algorithms at scale, often on cloud infrastructure, and present insights to stakeholders. This role sits in a collaborative Data Science Lab that partners across the organization to drive data-driven decisions in finance. Compensation / Benefits * Develop Python-based software to ingest data from databases, files, APIs and create training/validation/testing datasets * Analyze raw data with descriptive statistics and collaborate with domain experts to interpret fields * Build unit tests, data quality checks, and data pipelines to ensure trusted data for models * Explore supervised/unsupervised/time-series ML methods and ensemble approaches for robust performance * Tune ML models by selecting hyper-parameters and feature subsets * Identify data biases or leakage and ensure realistic train/test splits * Run large-scale training/inference on private/public cloud infrastructure * Present findings to internal/external customers using ML metrics and business language * Provide guidance to software teams as Data Science prototypes transition to production * Contribute across the research lifecycle from hypothesis to ETL-era software and results * Plan/host data science training sessions and hackathons * Collaborate with external vendors/universities to incorporate new techniques * Solve complex problems and bring new perspectives to existing solutions * Exercise judgment across multiple information sources and influence activities within and across teams * Operate within broad guidelines and contribute to organizational goals Tasks * Python and core libraries (numpy, pandas, sklearn) * Linux and basic development tools * Advanced distributed ML frameworks (Keras, TensorFlow) * Azure cloud infrastructure (preferred) * Strong statistical background in Computer Science * Data science knowledge with an emphasis on practical application in finance * Ability to lead small projects and mentor others Key requirements * retirement benefits (401k and pension) * health and welfare benefits * paid time off * parential and caregiver leave * life & accident insurance * discretionary bonus program with potential equity component

by selecting hyper-parameters and feature subsets * Identify data biases or leakage and ensure realistic train/test splits * Run large-scale training/inference on private/public cloud infrastructure * Present findings to internal/external customers using ML metrics and business language * Provide guidance to software teams as Data Science prototypes transition to production * Contribute across the research lifecycle from hypothesis to ETL-era software and results * Plan/host data science training sessions and hackathons * Collaborate with external vendors/universities to incorporate new techniques * Solve complex problems and bring new perspectives to existing solutions * Exercise judgment across multiple information sources and influence activities within and across teams * Operate within broad guidelines and contribute to organizational goals Tasks * Python and core libraries (numpy, pandas, sklearn) * Linux and basic development tools * Advanced distributed ML frameworks (Keras, aaa testing. * Azure cloud infrastructure (preferred) * Strong statistical background in Computer Science * Data science knowledge with an emphasis on practical application in finance * Ability to lead small projects and mentor others Key requirements * retirement benefits (401k and pension) * health and welfare benefits * paid time off * parential and caregiver leave * life & accident insurance * discretionary bonus program with potential equity component