The Team
The Innovation & Delivery Transformation team is building the future of our business. This the group responsible for identifying, nurturing, scaling, and ultimately winning in new markets with new capabilities. We don’t rely on what the firm has always done – we look to the future and invest where we know the growth will be 3, 5, 10 years down the road. The Innovation & Delivery Transformation Team collaborates with Industries, Offering Portfolios, Growth, and Delivery Transformation to curate and scale our Innovation Portfolio, grow through our Strategic Growth Offerings, identify and incubate the next generation of technology through our Technology Offices, and more. Together we are dedicated to fostering a top talent experience both within I&DT and throughout our firm by leading our Tech Talent Transformation.
ConvergeCONSUMER is building the Decision OS for the Modern Consumer Business, helping companies in grocery, retail, CPG, restaurants, and sports make smarter and faster decisions at machine speed with operator precision. We deliver AI-native products like Pulse, Signals, Forecasting, Identity, Segmentation, Pricing and Promotion Optimization, Inventory, and Store Clustering, along with vertical solutions such as the Restaurant Insights Portal and Sports Fan App. Powered by a proprietary data fabric of 7000 plus features across 100 plus sources, our platform compounds intelligence with every decision made, overridden, or rejected. We move fast, tie our outcomes directly to client impact, and operate with radical candor, vertical obsession, and a bias for proof of value. Joining ConCON means shaping a new category in decision intelligence and directly driving growth, efficiency, and customer loyalty for the world’s leading brands.
Recruiting for this role ends on 10/10/2025.
Work You’ll Do
As an Optimization Data Scientist, within Deloitte Consulting’s ConvergeCONSUMER™ team, you will develop and deploy advanced optimization models that drive strategic decision-making for leading consumer-focused businesses. Leveraging expertise in mixed integer programming, linear programming, and derivative-free optimization, you will formulate complex business challenges into scalable mathematical solutions using tools like Python, Gurobi, and IPOPT. This role sits at the intersection of applied mathematics, data science, and product engineering, enabling you to build high-impact solutions that integrate seamlessly into enterprise systems and deliver measurable value in personalization, operational efficiency, and growth.
- 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.
- Represent the optimization function with senior stakeholders and clients, articulating trade-offs, constraints, and business impacts of proposed solutions.
- Contribute to the innovation agenda by exploring emerging techniques such as reinforcement learning, digital twins, and Bayesian optimization to enhance the platform’s decision-making edge.
Key Responsibilities
- Design, develop, and deploy advanced optimization models (MIP, LP, DFO, stochastic and robust methods) to solve complex consumer business 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, ensuring alignment with product vision and client outcomes.
- Integrate optimization engines into production environments through APIs, microservices, and cloud-based workflows (AWS, GCP, Azure).
- Collaborate with platform engineering to ensure scalable, secure, and efficient deployment using containerization (Docker, Kubernetes) and CI/CD pipelines.
- Maintain and improve solver performance across Gurobi, CPLEX, IPOPT, and open-source frameworks, ensuring solutions run efficiently at enterprise scale.
- Write clean, efficient, and reusable Python code using libraries such as Pyomo, PuLP, CVXPY, SciPy.optimize, NumPy, and Pandas.
- Drive adoption of optimization models by creating explainable outputs, validating results with domain experts, and ensuring usability for business stakeholders.
- Contribute to the evolution of ConvergeCONSUMER’s Optimization Center of Excellence by mentoring junior team members, codifying best practices, and advancing new techniques (reinforcement learning, digital twins, Bayesian optimization).
- Engage directly with US-based clients and internal stakeholders (CCO, product owners, delivery teams) to ensure solutions deliver measurable business impact.
- Safeguard continuity in optimization capability development, ensuring ongoing investments in ConvergeCONSUMER models and infrastructure translate into tangible client outcomes.
The successful candidate will possess
- 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.
- Knowledge of CI/CD pipelines, software engineering best practices, and cloud environments (AWS, GCP, Azure).
- Experience working with platform engineering teams to integrate models into enterprise-scale systems.
- Strong understanding of consumer-focused business challenges in areas like assortment planning, pricing, personalization, and forecasting.
- Ability to collaborate with product managers to align technical solutions with product vision and roadmap.
- Experience conducting trade-off analysis to support decision-making under competing business constraints.
- Exposure to reinforcement learning or hybrid ML and optimization workflows.
- Familiarity with Bayesian optimization or metaheuristics.
- Understanding of data governance and model explainability requirements.
Qualifications
Required:
- 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.
- 5+ years of hands-on experience deploying optimization models into production systems, APIs, or microservices.
- 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.
Preferred:
- 4+ years’ experience with simulation modeling or digital twin environments.
- Advances degree.
The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Deloitte, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is $124,700-229,500.
You may also be eligible to participate in a discretionary annual incentive program, subject to the rules governing the program, whereby an award, if any, depends on various factors, including, without limitation, individual and organizational performance.
Information for applicants with a need for accommodation: https://www2.deloitte.com/us/en/pages/careers/articles/join-deloitte-assistance-for-disabled-applicants.html
