Data Science Manager
SentiLinkSalary not specified
Full Time
Expert & Leadership (9+ years)
Not SpecifiedCompensation
Senior (5 to 8 years), Expert & Leadership (9+ years)Experience Level
Full TimeJob Type
UnknownVisa
Retail, Marketing, Data ScienceIndustries
Requirements
- 2+ years focused on implementing and deploying complex causal/statistical models for business impact
- Expert-level experience with Markov Chain Monte Carlo (MCMC) methods and associated libraries (e.g., PyMC)
- Proven experience in designing and deploying Agentic AI workflows or Generative AI systems (e.g., LLMs, multi-agent frameworks) to automate and optimize data science pipelines
- Deep understanding of diverse machine learning algorithms, including Deep Learning (Neural Networks, PyTorch/TensorFlow) and Reinforcement Learning concepts
- PhD or Masters in Computer Science, Statistics, Mathematics, or a highly quantitative field with 5+ years of relevant industry experience
- Advanced statistical expertise, full-stack machine learning engineering, and strategic leadership
Responsibilities
- Pioneer Bayesian MCMC MMM: Design, implement, and productionize a scalable, Bayesian Hierarchical MMM framework (using tools like PyMC), including rigorous feature engineering, defining priors, implementing sophisticated MCMC sampling techniques, and developing robust methods for handling time-varying effects, diminishing returns, and competitive factors
- Architect Agentic AI Workflows: Develop and deploy multi-agent systems (Agentic AI) to automate the entire measurement lifecycle, involving automating data ingestion and cleaning, model version control, adaptive prior selection, and dynamic insight generation from the MMM results
- Drive Probabilistic Forecasting & Simulation: Utilize the full posterior distribution from the Bayesian models to generate probabilistic forecasts and comprehensive scenario simulations, enabling Marketing and Finance teams to make data-driven, risk-aware investment decisions
- Integrate Causal Insights: Integrate MMM output with other causal inference models (e.g., A/B tests, Geospatial Experiments, uplift models) to create a unified, holistic view of marketing performance and decompose brand vs. sales effects
- Scale and Operationalize: Partner closely with Engineering teams to transition models from research prototypes to highly reliable, performant, and automatically updating production systems that serve real-time investment recommendations to marketing platforms
- Influence Strategy: Communicate complex probabilistic and causal insights clearly to C-suite and Senior Leadership, translating modeling results into clear, actionable budget allocation and tactical campaign optimization strategies
Skills
Key technologies and capabilities for this role
Questions & Answers
Common questions about this position
What compensation is offered for this role?
Walmart offers competitive pay as well as performance-based opportunities.
Is this position remote or does it require working in an office?
This information is not specified in the job description.
What key skills are required for this Senior Manager role?
The role requires Bayesian & MCMC mastery with 2+ years implementing complex causal/statistical models using tools like PyMC, proven experience in Agentic AI workflows or Generative AI systems, deep understanding of machine learning algorithms including Deep Learning and Reinforcement Learning, plus expert-level MCMC experience.
What is the education requirement for this position?
Candidates need a PhD or Masters in Computer Science, Statistics, Mathematics, or a highly quantitative field with 5+ years of relevant industry experience.
What makes a strong candidate for this Data Science Manager role?
A strong candidate brings a rare blend of advanced statistical expertise, full-stack machine learning engineering, and strategic leadership, including 2+ years focused on Bayesian MCMC models and experience with Agentic AI workflows.
Walmart
Global retail chain offering affordable products
About Walmart
Walmart operates a chain of hypermarkets, discount department stores, and grocery stores, providing a wide variety of products at low prices to everyday consumers, families, and communities. The company combines physical retail locations with an e-commerce platform, allowing customers to shop for groceries, apparel, electronics, and household items both in-store and online. Walmart's efficient supply chain management and large-scale purchasing enable it to maintain affordability, setting it apart from competitors. The company also offers financial services and health and wellness services, including pharmacies and vision centers. Walmart's goal is to save people money and support communities, as demonstrated by its efforts during the COVID-19 pandemic and its commitment to social responsibility, including programs for veterans and military families.
Bentonville, ArkansasHeadquarters
1962Year Founded
$5,515MTotal Funding
IPOCompany Stage
Financial Services, Consumer GoodsIndustries
10,001+Employees
Benefits
PTO: Paid vacation, sick time, personal time and holiday time
10% discount on regularly priced general merchandise and fresh fruits and vegetables
6% 401(k) match to all employees, including hourly workers, after one year
Roth IRA available
Associate Stock Purchase Plan
maximum and eligible preventive care covered at 100%
Health reimbursement plans
Risks
Rising U.S. labor costs could pressure profit margins.
Amazon's grocery delivery expansion threatens Walmart's market share.
Fintech investments expose Walmart to financial regulatory risks.
Differentiation
Walmart's omnichannel strategy integrates physical and online retail seamlessly.
The company offers a wide range of products at consistently low prices.
Walmart's commitment to community support enhances its brand reputation.
Upsides
Walmart's investment in fintech expands its financial services offerings.
Robotics integration in distribution centers boosts operational efficiency.
Culturally themed product lines attract diverse customer segments.
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