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# Software Engineering - Lead Data Scientist

- **Company:** Fair Isaac Corporation
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $105,000.0 - $165,000.0
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Airflow, Amazon Web Services, Amazon S3, Big Data, Code Review, Cyber Security, Continuous Integration, Cursor, Database Queries, Statistical Hypothesis Testing, Python (Programming Language), Machine Learning, NumPy, Release Management, OpenAI, Tensorflow, Standard Sql, Software Engineering, Jupyter Notebook, GitHub Copilot, Pytorch, Retrieval-Augmented Generation, Transfer Learning, Large Language Models, Claude Code, Prompt Engineering, Apache Spark, Generative AI, AI Coding Agents, Agentic-AI, Pandas, Pyspark, Scikit Learn, Information Technology, Xgboost, Performance Monitor, Machine Learning Operations, Claude, GPT, Data Pipelines, Docker
- **Published:** October 30, 2026
- **Apply:** https://fico.wd1.myworkdayjobs.com/External/job/Work-from-Home-United-States/Software-Engineering---Lead-Data-Scientist_32365

## About the Role

* Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or a related field, with demonstrated experience in data science, machine learning engineering, or applied AI, including models running in production.

 * Strong Python for data engineering and ML (pandas, NumPy, scikit-learn, PySpark), with comfort working in Jupyter notebooks.

 * Proven experience building data pipelines and processing large datasets with Spark, Redshift, or similar technologies, plus expert SQL skills for complex queries on large datasets.

 * Hands-on experience with ML frameworks such as XGBoost, TensorFlow, or PyTorch.

 * Experience operationalizing ML models, including deployment, monitoring, retraining, and CI/CD (SageMaker, MLflow, Docker, or similar), and running data and ML workloads in AWS (S3, Glue, Lambda, Bedrock, or similar).

 * Experience with LLMs (Claude, OpenAI GPT, or similar), including prompt design, evaluation, and deploying Generative AI solutions in production.

 * Strong grounding in statistics, experimental design, and hypothesis testing.

 * Production ownership, including code review, release management, and mentoring, with the ability to explain technical results to non-technical partners.

 * Experience with RAG workflows, vector stores, MCP server integrations, LLM fine-tuning, workflow orchestration (Airflow, Step Functions), or regulated consumer data such as credit and financial services is a plus.

## Description

The myFICO (Scores B2C) engineering team is seeking a Lead Data Scientist to build and run machine learning and Generative AI solutions in our consumer applications. This lead-level software engineering role builds the data pipelines that prepare large datasets for modeling, develops ML models and puts them into production with monitoring and retraining, and measures business impact through experimentation. You will also set ML engineering best practices for the team.

What You'll Contribute

 * Build and maintain Python data pipelines that ingest, clean, and transform large datasets into reliable, analysis-ready data and ML features.

 * Profile and validate data, and put quality checks in place so models and analyses can be trusted.

 * Develop machine learning models, such as propensity, churn, segmentation, and forecasting, from problem framing through evaluation.

 * Deploy models to production in AWS for batch and real-time scoring, monitor performance and drift, and automate retraining.

 * Design, evaluate, and improve LLM prompts, and integrate Generative AI capabilities into existing applications.

 * Design and analyze A/B tests to measure business impact, and explain results to product and marketing partners.

 * Lead design discussions and code reviews, mentor engineers and analysts, and set ML engineering best practices.

 * Partner with database engineering, product management, and InfoSec to build secure, compliant solutions that handle sensitive consumer data responsibly.

 * Use AI coding assistants and agentic workflows, such as Claude Code, Cursor, and GitHub Copilot, to accelerate delivery while reviewing and refining their output.