Lead two data science sub-streams supporting commercial teams, owning technical direction and delivery of predictive, causal and optimization solutions.
Responsibilities
Lead, mentor and develop Senior Data Scientists and Data Scientists across both sub-streams
Build technical depth, modular coding standards and agile delivery habits
Lead the design, calibration and validation of Bayesian and frequentist Marketing Mix Models (MMM)
Oversee multi-touch attribution (MTA) and brand performance analytics
Design and govern experiments, including A/B tests, quasi-experimental designs and econometric analysis
Requirements
Bachelor's or Master's degree in Data Science, Statistics, Economics, Artificial Intelligence and Machine Learning, Computer Science, Business Analytics or another quantitative field
Own the modelling and data science for ASICS' enterprise and operations domains — Supply Chain, Factory Planning, and Finance, building and elevating forecasting, planning, and optimisation models.
Responsibilities
Own enterprise & operations modelling
Build agentic and natural-language capabilities, including Conversational BI
Partner with senior stakeholders across Supply Chain, Planning, Operations, Manufacturing, Procurement, and Finance teams
Provide technical leadership and mentoring
Ensure model quality, governance, and production readiness
Requirements
Degree in a quantitative discipline such as statistics, mathematics, operations research, industrial engineering, data science or computer science
The Postdoctoral Scholar will play a leading role in modeling and forecasting the hiring and retiring workforce trends in the utility sector using machine learning models, and help develop an innovative forecasting platform.
Responsibilities
Lead analyses to forecast state-level and national electric utility workforce trends (e.g., in 5 and 10 years) related to retirement and hiring
Manage large-scale education, labor market, and energy-sector datasets
Develop and compare forecasting approaches, including traditional time-series models (e.g., ARIMA, VAR) and machine learning methods (e.g., Random Forest, LSTM)
Contributes to analyses examining workforce resilience under alternative energy, climate, and labor market scenarios
Support other analyses examining workforce behaviors, including knowledge transfer and sharing within utility organizations
Requirements
Ph.D. in Economics, Demography/Population Studies, Statistics, Data Science, Public Policy, Labor and Employment Relations/Industrial Relations, Workforce Development, Business Analytics/Management Science, Sociology, or a closely related field
Nice to have: Workforce or demographic forecasting or labor market projections using machine learning approaches
Nice to have: Experience using federal or state employment, labor, and/or education datasets
This is not a pure research role but one that is actively involved in building our product. You will be joining a team of Data Science Engineers and you will be directly contributing to code. You will own well-scoped modeling problems end to end, from exploring market data and designing features to validating models and getting them running in production, so that our pricing, inventory, and network optimization engines make better decisions in real time. This is a high-impact individual contributor role at the intersection of applied AI and production software, working directly with other data scientists, ML and data engineers, and product teams.
Responsibilities
Develop, validate, and iterate on demand forecasting, pricing, and optimization models using large-scale, high-velocity market data.
Explore and analyze data from diverse internal and external sources to uncover signals, define features, and identify opportunities to improve model accuracy and business impact.
Design and run offline experiments, backtests, and online evaluations to measure model performance and profit uplift.
Write production-grade Python code and work with ML and data engineers to productionize models, ensuring they are reliable, observable, and cost-efficient.
Monitor deployed models for drift, accuracy, and data quality issues, and troubleshoot and fix problems quickly.
Lead the Data Science Products team: the team that turns proven data science capabilities into owned, governed, reusable products and evolves them over time (v1.0 → N). The portfolio is the measurement, optimization, and forecasting engine behind commercial investment decisions across USA and International: marketing mix and incrementality measurement, promotional and media investment optimization, and forecasting.
Locations (2)
New York City, New York, United States
United States
Compensation
The annual base salary for this position ranges from $230,900.00 to $384,800.00. In addition, this position is eligible for participation in Pfizer’s Global Performance Plan with a bonus target of 25.0% of the base salary and eligibility to participate in our share based long term incentive program.
Responsibilities
Own the strategy, roadmap, and health of the full product portfolio across measurement, optimization, forecasting and more
Stand up and run the global/shared model: methodology standards, the shared codebase, intake and prioritization, quality standards, and best-practice forums
Own the standard for how commercial impact is measured, versioned, and validated across the portfolio
Hands-on leadership role combining technical depth, people leadership, and strong business partnership to turn complex commercial challenges into scalable data science solutions, leading data science delivery across products and markets within the Customer Development (CD) portfolio.
Compensation
The pay range for this position is $103,000 to $154,400. This position is bonus eligible. Long-Term Incentive (LTI): This position is LTI eligible.
Responsibilities
Own and evolve the data science roadmap for products across the Customer Development (CD) portfolio
Lead the design, development, and deployment of diagnostic, predictive, and prescriptive analytics solutions
Set high modelling and analytical standards across classical statistics, machine learning, and Generative and Agentic AI capabilities
Ensure solutions are production-ready and industrialised using cloud platforms such as Azure and Databricks
Build a culture of ownership, curiosity, continuous improvement, and disciplined delivery
Lead the creation and operation of a complex, constraint-based optimization and recommendation ecosystem supporting the bank’s operations, integrating time-series predictions and demand forecasts with optimization models.
Compensation
We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions.
Responsibilities
Lead the design and delivery of a complex constraint-optimization recommendation system
Formulate global, system-wide optimization objectives and constraints
Develop and validate time-series and demand-driven predictive models
Build and lead a multidisciplinary team of applied AI/ML researchers, optimization specialists, and engineers
Drive significant business stakeholder management across executive leadership and product, business, analytics, data, and engineering partners
Requirements
PhD in Computer Science in Artificial Intelligence, Operations Research, or a closely related quantitative discipline
Nice to have: advanced research or specialized doctoral work at the intersection of AI/ML, time-series forecasting, demand modeling, and constraint optimization
Nice to have: deep experience with distributed infrastructure
Nice to have: deep experience with distributed ML and scalable optimization
Nice to have: deep experience with production model lifecycle management
Set and review modeling methodology across the portfolio: Bayesian and causal marketing mix modeling, constrained optimization, incrementality and experiment design, and time-series and hierarchical forecasting
Lead the evolution of each product line from analyst-run engine to agentic solution
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