Track This Job
Add this job to your tracking list to:
- Monitor application status and updates
- Change status (Applied, Interview, Offer, etc.)
- Add personal notes and comments
- Set reminders for follow-ups
- Track your entire application journey
Save This Job
Add this job to your saved collection to:
- Access easily from your saved jobs dashboard
- Review job details later without searching again
- Compare with other saved opportunities
- Keep a collection of interesting positions
- Receive notifications about saved jobs before they expire
AI-Powered Job Summary
Get a concise overview of key job requirements, responsibilities, and qualifications in seconds.
Pro Tip: Use this feature to quickly decide if a job matches your skills before reading the full description.
You’ll design and implement data science and machine learning solutions that blend cutting-edge research with practical deployment. Working across industries, domains, and use cases, you’ll tackle challenging analytical problems, build robust models, and deliver insights that power client-facing tools and internal platforms.
- Develop End-to-End Models: Design, train, and evaluate models for prediction, classification, optimization, or inference—taking projects from exploratory analysis through production deployment.
- Collaborate on Real-World Solutions: Partner with software engineers, analysts, and fellow data scientists to integrate data-driven models into scalable, deployable systems that operate in dynamic production environments.
- Client-Focused Problem Solving: Work closely with stakeholders to frame ambiguous problems, explore solution paths, and translate complex technical insights into clear, actionable recommendations.
- Explore, Iterate, Validate: Lead the exploration and analysis of large and diverse datasets using tools like Pandas, NumPy, and Spark to inform model design, evaluate performance, and identify opportunities for improvement.
- Research-Driven Innovation: Stay current with advances in statistics, machine learning, and data engineering practices—adapting new methods and technologies to deliver measurable impact on real-world problems.
Professional Experience
- 2+ years of hands-on experience building and deploying data science or machine learning models in production environments.
- Proven ability to take models from prototype to production using Python-based workflows.
- Experience engaging with technical and non-technical stakeholders to refine requirements and communicate results effectively.
- Strong proficiency in Python and commonly used ML/data libraries (Pandas, NumPy, scikit-learn, PyTorch, or similar).
- Experience working with large-scale datasets and distributed tools such as Spark.
- Comfort navigating cloud environments (e.g., GCP, AWS, or similar); Databricks experience is a plus.
- Solid understanding of statistical modeling, experimental design, model evaluation, and data debugging practices.
- Strong code hygiene — able to write clean, modular, and testable code in collaborative, version-controlled environments.
Important
All candidates must pass an interview as part of the contracting process.
Key Skills
Ranked by relevanceReady to apply?
Join Invisible Expert Marketplace and take your career to the next level!
Application takes less than 5 minutes

