Role: Data Scientist
Location: Atlanta, GA
Mode Of Hire: Full Time
Key Responsibilities
• Data wrangling & feature engineering: Ingest, clean, and transform data from SQL, APIs, and data lakes (e.g., Snowflake, Databricks). Design robust pipelines that feed into analytics and ML workflows.
• Data understanding & exploration: Work closely with domain experts to deeply understand the meaning, context, quality, and limitations of available datasets. Translate business questions into data requirements and analytics plans.
• Machine learning development: Build, tune, and validate predictive models using scikit-learn, SparkML, XGBoost, or TensorFlow.
• Cross-functional partnership: Collaborate with marketing, sales, and product teams to scope business use cases, define success metrics, and integrate models into operational workflows.
• Model deployment & MLOps: Deploy and manage models using MLflow, docker and CI/CD pipelines. Implement versioning, testing, performance monitoring, and retraining strategies as part of a robust MLOps practice.
• Infrastructure support: Work with data engineering and DevOps teams to maintain and improve model training and deployment infrastructure, including compute resources, workflow orchestration and environment configuration.
• Insight delivery: Build clear, actionable reporting and visualizations using tools like Power BI or Tableau. Focus on impact, not just analysis.
Skills Required:
• Bachelor’s degree in Data Science, Computer Science, Engineering, or a related quantitative field.
• 5+ years of experience in a data science, ML engineering, or analytics role.
• Strong SQL, Python and ML Techniques programming skills.
• Experience with Azure Cloud, Databricks, and/or Snowflake.
• Experience building and deploying machine learning models in production environments. Hands-on experience with Databricks, including SparkML, and MLflow integration.
• Familiarity with MLOps best practices, including version control, model monitoring, and automated testing.
• Experience with tools such as Git, MLflow, Docker and workflow schedulers.
• Ability to communicate complex technical work to non-technical stakeholders.
• Experience with scalable model training environments and distributed computing.
Preferred Qualifications
• Master’s degree in a quantitative or technical discipline.
• Experience in financial services, fintech, or enterprise B2B analytics.
• Knowledge of A/B testing, causal inference, and statistical experimentation.
• Familiarity with GenAI, LLM pipelines, and vector-based retrieval is a plus and platform like Snowflake Cortex.
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