Python / Data Science Developer
Job Description
About the Role
We are seeking a skilled Python / Data Science Developer to develop production-grade machine learning solutions using Pandas, NumPy, Scikit-learn. This is not a research-only role — you'll take models from concept through training, validation, and deployment to production environments serving real users. The ideal candidate brings a strong foundation in statistical learning, experience with Jupyter and MLflow, and a track record of deploying ML systems that deliver measurable business impact. You'll collaborate with data engineers, product managers, and business stakeholders to identify high-value AI opportunities and ship solutions that scale.
Key Responsibilities
- Own Pandas implementation and optimization — configuration, customization, and ongoing enhancement based on business needs
- Manage NumPy workflows including setup, user training, and continuous improvement of processes
- Implement and maintain Scikit-learn ensuring seamless integration with existing systems and workflows
- Design and train machine learning models using Python / Data Science best practices and modern architectures
- Build and maintain data pipelines that feed ML models with clean, validated training data
- Deploy models to production with monitoring, alerting, and automated retraining capabilities
- Collaborate with product teams to identify high-impact AI use cases and estimate feasibility
- Conduct model performance analysis — precision, recall, latency, and business impact metrics
- Document model architectures, training procedures, and serving infrastructure for team knowledge sharing
- Stay current with Python / Data Science advances and evaluate new techniques for potential adoption
Must-Have Qualifications
- Hands-on experience with Pandas — configuration, customization, and troubleshooting in production environments
- Proficiency with Jupyter as part of the Python / Data Science development/operations workflow
- 3+ years of Python / Data Science experience with models deployed to production environments
- Strong foundation in statistics, linear algebra, and probability theory
- Experience with the full ML lifecycle — data preparation, training, validation, deployment, and monitoring
- Proficiency in Python and common ML frameworks (TensorFlow, PyTorch, or scikit-learn)
- Understanding of MLOps practices — versioning, reproducibility, and automated pipelines
Nice-to-Have Skills
- AWS ML Specialty certification or equivalent validated credential
- Google ML Engineer certification or equivalent validated credential
- Experience with advanced Python / Data Science features: NumPy, Scikit-learn, TensorFlow
- Familiarity with the broader Python / Data Science ecosystem including MLflow and Airflow
- Published research, conference presentations, or open-source ML contributions
- Experience with large language models (LLMs), RAG architectures, or generative AI applications
Interview Tips
End-to-End ML Design
Present a business problem and ask the candidate to design a complete Python / Data Science solution — data requirements, model approach, evaluation metrics, and deployment plan.
Code Review Exercise
Show them a Python / Data Science code snippet with subtle issues (data leakage, incorrect validation split). See if they catch them and explain the implications.
Production Debugging
Describe a scenario where a deployed Python / Data Science model's performance has degraded. Ask them to walk through their diagnostic process step by step.
Technical Presentation
Ask them to present a past Python / Data Science project — problem, approach, results, and what they'd do differently. Evaluate technical depth and communication clarity.
Typical Team Structure
Team Size
2-4 Python / Data Science engineers
Reports To
Head of AI/ML, VP of Engineering, or CTO
Collaborates With
Data Engineering, Product Management, Backend Engineering, Business Intelligence
Skip the JD — Get Matched Instead
Tell us your Python / Data Science requirements and we'll send pre-vetted profiles with video intros in 24-48 hours.
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