Data Scientist

Role & Responsibilities

• Collect, clean, and analyze large structured and unstructured datasets from multiple internal and external sources
• Conduct thorough exploratory data analysis (EDA) to understand data distributions, relationships, outliers, and missing value patterns
• Profile and audit datasets to assess data quality, completeness, consistency, and fitness for modeling
• Investigate and document data lineage — understanding where data originates, how it flows, and how it transforms across systems
• Identify and resolve data anomalies, inconsistencies, and integrity issues in collaboration with data engineering teams
• Develop a deep understanding of the business domain and the underlying data that represents it — including what each field means, how it is captured, and what its limitations are
• Translate raw, messy, real-world data into clean, well-understood analytical datasets ready for modeling and reporting
• Apply statistical techniques such as correlation analysis, hypothesis testing, variance analysis, and distribution fitting to extract meaningful signals from noise
• Build and deploy machine learning models including regression, classification, clustering, NLP, and time-series analysis
• Design, evaluate, and analyze A/B experiments and controlled tests using causal inference techniques
• Develop data-driven recommendations backed by rigorous statistical reasoning
• Write clean, production-ready code in Python or R
• Collaborate with data engineers to build reliable data pipelines and feature stores
• Deploy and monitor ML models using MLOps best practices on cloud infrastructure
• Build dashboards and self-serve analytics tools to support stakeholder decision-making

Data Understanding & Analysis Skills

• Strong ability to interrogate unfamiliar datasets and quickly develop a working understanding of their structure, semantics, and quirks
• Experience working with messy, incomplete, or poorly documented real-world data
• Skilled in identifying hidden patterns, trends, seasonality, and anomalies through visual and statistical exploration
• Able to ask the right questions about data — challenging assumptions, validating sources, and understanding the context in which data was collected
• Proficiency in data profiling, descriptive statistics, and summary reporting to communicate the shape and health of a dataset
• Experience creating data dictionaries, documentation, and data quality reports to support team-wide data understanding
• Comfort working across structured (relational tables), semi-structured (JSON, XML), and unstructured (text, logs, sensor streams) data formats

Technical Skills Required

• Proficiency in Python (pandas, NumPy, scikit-learn, PyTorch or TensorFlow) and/or R
• Strong SQL skills with hands-on experience in DB2 and SQL Server
• Experience with Databricks for large-scale data processing, feature engineering, and model training
• Familiarity with cloud platforms: Azure or AWS
• Experience with data warehouses and big data platforms (Databricks, Snowflake, or Redshift)
• Knowledge of MLOps tools such as MLflow, Kubeflow, or Airflow
• Experience with streaming data technologies such as Kafka or Spark
• Solid foundation in probability, statistics, linear algebra, and experimental design

Nice to Have

• Experience with deep learning, NLP, computer vision, or Bayesian methods
• Familiarity with real-time or streaming data pipelines
• Open-source contributions or published research

Compensation Range

$100K - $145K