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:
Bachelors 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
Masters 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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