Architects of Adaptive Intelligence for the Enterprise
Data Scientist
Location
United States
Posted
4 days ago
Salary
$100K - $145K / year
Seniority
Senior
Job Description
Data Scientist
Gradera
• 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
Job Requirements
- 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
- Ability 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
- 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
- Experience with deep learning, NLP, computer vision, or Bayesian methods (Nice to Have)
Benefits
- Offers Equity
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