Lead Data Scientist – Software Engineering

This is a fully remote position, open to applicants in United States.

📋 Description

• Develop and sustain Python data pipelines that acquire, cleanse, and convert extensive datasets into trustworthy, analysis-ready data and machine learning features.

• Analyze and validate data while implementing quality assurance measures.

• Create machine learning models for propensity, churn, segmentation, and forecasting, from problem formulation to evaluation.

• Deploy models on AWS for both batch processing and real-time scoring.

• Track model performance and drift, automating the retraining process.

• Design, assess, and enhance LLM prompts.

• Incorporate Generative AI functionalities into current consumer applications.

• Design and evaluate A/B tests to assess business impact.

• Communicate findings to product and marketing teams.

• Facilitate design discussions and conduct code reviews.

• Mentor engineers and analysts while establishing best practices in ML engineering.

• Collaborate with database engineering, product management, and InfoSec to create secure, compliant solutions for sensitive consumer data.

• Utilize AI coding assistants and agentic workflows, including Claude Code, Cursor, and GitHub Copilot, while reviewing and refining their outputs.

⛳️ Requirements

• Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, or a related discipline.

• Proven experience in data science, machine learning engineering, or applied AI, particularly with models in production.

• Proficient in Python for data engineering and ML, including libraries such as pandas, NumPy, scikit-learn, and PySpark.

• Comfortable working within Jupyter notebooks.

• Experience in constructing data pipelines and processing large datasets using Spark, Redshift, or similar technologies.

• Advanced SQL capabilities for complex queries on large datasets.

• Practical experience with XGBoost, TensorFlow, or PyTorch.

• Familiarity with operationalizing ML models, encompassing deployment, monitoring, retraining, and CI/CD practices.

• Knowledge of tools such as SageMaker, MLflow, Docker, or similar platforms.

• Experience managing data and ML workloads on AWS, including services like S3, Glue, Lambda, and Bedrock or similar offerings.

• Experience with LLMs such as Claude or OpenAI GPT, including prompt design, evaluation, and deployment of Generative AI solutions in production.

• Strong foundation in statistics, experimental design, and hypothesis testing.

• Experience with production ownership, including code review, release management, and mentoring.

• Ability to articulate technical outcomes to non-technical stakeholders.

• Familiarity with RAG workflows, vector stores, MCP server integrations, LLM fine-tuning, workflow orchestration, or regulated consumer data is a plus.

🏝️ Benefits

• Highly competitive compensation, benefits, and rewards programs.

• Work/life balance.

• Employee resource groups.

• Social events.

• Valuable learning experiences.

• Professional development opportunities.

• Inclusive, people-first work environment.