Highlights
Join our cutting-edge team, where you’ll tackle challenging ML problems and develop innovative solutions.
Description
Job Summary
pAre you a talented Data Scientist looking to build machine learning solutions for complex decision-making problems? Join our team where you will work on innovative projects involving optimization, deep reinforcement learning, and predictive modeling. This is an opportunity to apply your expertise in real-world scenarios while collaborating with a multidisciplinary team.
Responsibilities
- Build ML models for planning, sequencing, routing, allocation, and resource utilization problems.
- Prototype solutions using agentic coding tools like Claude Code-style workflows.
- Develop and evaluate optimization and deep reinforcement learning models.
- Create robust evaluation harnesses to test model performance offline and in real-world scenarios.
- Collaborate with ML engineers to support productionization of models, ensuring latency/throughput constraints are met.
- Document findings and communicate results to both technical and non-technical stakeholders.
Required Skills
- Python programming
- Machine learning frameworks (PyTorch preferred)
- Data modeling and optimization techniques
- Agentic coding tools experience
- Problem-solving and experimentation design
Required Skills Explained
- Experience with applied machine learning, data science, and applied research.
- Familiarity with agentic coding tools like Claude Code to accelerate development iterations without compromising code quality.
- Solid Python skills and proficiency in ML tooling such as PyTorch or TensorFlow.
- A strong foundation in algorithms, probability/statistics, and experimental design.
- Ability to formulate real-world problems clearly and define measurable success metrics.
- Experience with optimization techniques like MILP/CP-SAT, heuristics, constraint programming, and search methods.
- Knowledge of Deep RL and Decision Intelligence methods including PPO/SAC/DQN, offline RL, imitation learning, MCTS, and policy/value learning.
- Expertise in building evaluation harnesses using simulation-based testing and counterfactual analysis.
- Prior work with ML engineering to support productionization through latency/throughput constraints, monitoring, reproducibility, model versioning, and safe rollout.
- Experience with MLOps basics including MLflow, Docker, CI/CD, and model monitoring.
- Comfort working in cloud platforms for deployment and management of machine learning models.
Who is this for
pThis role is ideal for someone with a passion for applying machine learning to real-world decision-making problems. You should have experience in applied ML/data science and be comfortable working on complex projects that require both technical expertise and strategic thinking.
Why This Job is a Good Opportunity
ulliGrowth potential within a dynamic field that combines advanced technology with real-world problem-solving.liOpportunity to work on cutting-edge ML projects involving decision-making algorithms, optimization, and deep RL.liCollaborative environment where you can learn from experienced professionals and contribute to impactful solutions.liFlexible development processes leveraging agentic coding tools for rapid prototyping and experimentation.liDiverse skill set required ensures continuous learning and adaptation in the evolving tech landscape.
Interview Preparation Tips
- Review your experience with applied machine learning projects, particularly those involving decision-making algorithms and optimization techniques.
- Prepare examples of how you have used agentic coding tools to enhance development efficiency while maintaining code quality.
- Discuss specific Python libraries and ML tooling (PyTorch or TensorFlow) that you are proficient in using.
- Highlight your understanding of key concepts like MILP, CP-SAT, heuristics, MCTS, and RL methods.
- Bring examples of how you have designed and executed robust evaluation harnesses for your projects.
- Cite instances where you have collaborated with ML engineers to support productionization.
- Prepare to explain any gaps in your experience through relevant coursework or self-study projects.
Career Growth in This Role
pThis role offers a pathway to senior positions in applied AI, particularly in areas of optimization and decision intelligence. You will have the opportunity to lead complex projects from conception to production, enhancing your expertise in both technical and business aspects of ML solutions. The exposure to diverse industries such as logistics, supply chain management, and industrial operations can also open up opportunities for specialized roles. Additionally, with continued growth, you might explore advanced MLOps practices or even venture into academic research.
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Skills
Frequently Asked Questions
What kind of projects will I be working on?You’ll work on projects involving optimization, deep reinforcement learning, and predictive modeling in real-world scenarios.
Do I need experience with simulation-based evaluation?Prior experience with simulation or digital twins is a plus but not required. We provide opportunities for learning.
What are the key skills needed for this role?Key skills include Python programming, machine learning frameworks like PyTorch, and agentic coding tools experience.
