Position Overview
Join Deloitte Consulting’s ConvergeCONSUMER™ team as an Optimization Data Scientist. As part of the Innovation & Delivery Transformation Team, you will develop and deploy advanced optimization models that drive strategic decision-making for leading consumer-focused businesses. You’ll help shape the future of decision intelligence through AI-native products and scalable solutions, working at the crossroads of applied mathematics, data science, and product engineering.
Recruiting for this role ends on 10/6/2025.
Key Responsibilities
- Design, develop, and deploy advanced optimization models (MIP, LP, DFO, stochastic, and robust methods) to address challenges such as assortment planning, pricing, promotion, and personalization.
- Translate ambiguous real-world problems into rigorous mathematical formulations that balance accuracy, scalability, and interpretability.
- Conduct trade-off and multi-objective optimization analyses to guide decision-making under competing business constraints.
- Partner with product managers and engineering teams to embed optimization solutions into ConvergeCONSUMER products via APIs, microservices, and cloud-based workflows.
- Collaborate with platform engineering to ensure scalable, secure, and efficient deployment using containerization and CI/CD pipelines.
- Maintain and improve solver performance across tools such as Gurobi, CPLEX, and IPOPT, ensuring enterprise-scale efficiency.
- Write clean, efficient, and reusable Python code using libraries like Pyomo, PuLP, CVXPY, SciPy.optimize, NumPy, and Pandas.
- Drive adoption of optimization models by creating explainable outputs and engaging with US-based clients and internal stakeholders.
- Mentor junior team members, codify optimization best practices, and contribute to the evolution of the Optimization Center of Excellence.
- Ensure continuity in optimization capability development, safeguarding investments that translate into tangible client outcomes.
Required Qualifications
- Proven ability to translate real-world business challenges into rigorous optimization models.
- Strong knowledge of both convex and non-convex optimization, including constraints handling and feasibility analysis.
- Familiarity with stochastic, robust, and multi-objective optimization techniques.
- Proficiency in writing efficient, well-documented Python code using relevant libraries (e.g., Pyomo, PuLP, CVXPY, SciPy.optimize, NumPy, Pandas).
- Expertise with optimization solvers such as Gurobi, CPLEX, IPOPT, GLPK, and COIN-OR.
- Experience with containerization (Docker, Kubernetes), CI/CD pipelines, and cloud environments (AWS, GCP, Azure).
- Bachelor’s degree and 5+ years of deep expertise in Mixed Integer Programming (MIP) and Linear Programming (LP).
- 5+ years of experience with Derivative-Free Optimization (DFO) methods and hands-on deployment of optimization models into production systems.
- Ability to travel 10-25% and must be legally authorized to work in the United States without employer sponsorship.
Preferred Qualifications
- 4+ years’ experience with simulation modeling or digital twin environments.
- Advanced degree.
- Exposure to reinforcement learning, digital twins, and Bayesian optimization techniques.
Benefits & Perks
- Compensation: Wage range of $124,700–$229,500, based on experience and various factors.
- Eligible to participate in a discretionary annual incentive program.
- Opportunity to work on innovative, AI-native products that directly impact leading consumer brands.
- Access to resources for professional development and career growth.
- For applicants needing accommodation, please review the accommodation information.