Deloitte's ConvergeCONSUMER team is looking to develop and deploy advanced optimization models to drive strategic decision-making for leading consumer-focused businesses, solving complex business challenges such as assortment planning, pricing, personalization, and forecasting.
Requirements
- Proven ability to translate real-world business challenges into rigorous optimization models.
- Strong knowledge of convex and non-convex optimization, constraints handling, and feasibility analysis.
- Familiarity with stochastic and robust optimization techniques.
- Experience with multi-objective optimization and trade-off analysis for complex decision problems.
- Proficiency in writing efficient, well-documented, and reusable Python code.
- Expertise with optimization libraries and solvers including Pyomo, PuLP, CVXPY, SciPy.optimize, NumPy, Pandas, Gurobi, CPLEX, IPOPT, GLPK, COIN-OR.
- Familiarity with containerization and deployment tools such as Docker and Kubernetes.
Responsibilities
- Own the end-to-end lifecycle of optimization capabilities within ConvergeCONSUMER, ensuring continuity, scalability, and adoption across multiple client engagements.
- Lead integration of optimization solutions into the broader Decision OS platform, working closely with platform engineering to embed models into APIs, microservices, and cloud-native workflows.
- Advance model explainability and decision transparency, enabling stakeholders to trust, adopt, and act on optimization outputs at scale.
- Manage performance tuning and solver strategy (e.g., Pyomo, Gurobi, CPLEX, IPOPT) to ensure efficiency on large-scale, high-frequency business problems.
- Partner with product leadership to align optimization roadmaps with strategic objectives, ensuring technical investments directly translate into measurable business outcomes.
- Mentor and upskill junior data scientists and engineers, codifying optimization best practices and building reusable frameworks to accelerate delivery.
- Proactively identify opportunities to extend optimization methods (stochastic, robust, multi-objective) into new use cases such as pricing, assortment planning, personalization, and supply chain.
Other
- Ability to travel 10-25%, on average, based on the work you do and the clients and industries/sectors you serve.
- Must be legally authorized to work in the United States without the need for employer sponsorship, now or at any time in the future.
- 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 such as genetic algorithms, pattern search, or surrogate modeling.
- Ability to collaborate with product managers to align technical solutions with product vision and roadmap.
