General Millsposted 5 days ago
$126,700 - $211,200/Yr
Full-time • Senior
Golden Valley, MN

About the position

As a Lead Data Scientist at General Mills, you will apply your strong expertise in Statistics, Mathematics, Data Science, AI, Machine Learning, data mining, and information retrieval to design, prototype, and build next-generation advanced analytics engines and services. You will collaborate with product owners, business partners, and other Data Scientists in the consumer marketing and media space to define a technical problem statement and hypothesis to test. In addition, you will develop efficient and accurate analytical models that mimic business decisions.

Responsibilities

  • Work closely with data science leadership, machine learning engineering, and business partners/product owners to develop optimization and machine learning models using best-in-class tools and technology.
  • Provide data science leadership within a product team through strong partnership, actionable insights, strategic and tactical technical roadmap recommendations.
  • Develop, validate, and maintain robust causal measurement strategies to quantify the effectiveness of various marketing channels, including Marketing Mix Models (MMM), Difference in Differences, Regression Discontinuity, and causal ML.
  • Conduct in-depth statistical and econometric analysis to measure marketing effectiveness, forecast the impact of future marketing activities, and recommend optimal budget allocation strategies.
  • Translate complex model insights and findings into clear, actionable recommendations for campaign planning, budget allocation, and marketing strategy to diverse stakeholders, including marketing, finance, and senior leadership.
  • Design, recommend, and execute statistically-proven A/B Testing for Marketing campaigns.
  • Counsel and advise business partners on technical solutions.
  • Serve as a senior leader as part of the data science leadership team- contribute to technical strategy, best practices, and team development.
  • Coach junior data scientists on key technical and domain topics through solution reviews and mentoring.
  • Responsible for monitoring and improving model performance metrics in terms of accuracy, optimal / near-optimal recommendations, with feedback incorporated from business partners.
  • Be a part of the team, collaborate, ask questions, engage, and solicit feedback from other Data Scientists.
  • Participate in activities such as strategic planning and technical standards definition, modeling documentation.

Requirements

  • 6+ years of professional experience working as a hands-on coding Data Scientist with 4+ years of professional experience working as a hands-on coding Data Scientist building Marketing Mix Models (MMM) and Marketing Attribution Models.
  • Bachelor’s Degree in Mathematics, Statistics, Data Science, Operations Research, Econometrics, or related quantitative discipline required.
  • Expert-level programming in Python and SQL.
  • Expertise in causal modeling principles (DAG design, assumptions of key designs, falsification), regression modeling (panel, time series, ridge), Bayesian modeling (e.g., PyMC), marketing mix model frameworks (PyMC, Meridian, Robyn), Ensemble (Gradient Boosting, XGBoost).
  • Comfortable working with large datasets & writing complex SQL queries.
  • Proven track record of partnership with marketing and business leaders, influencing key marketing decisions.
  • Highly adaptable, curious, and willing to work independently on complex and challenging problems.
  • Bias for action with the ability to deliver outstanding results through task prioritization and time management.
  • Experience with cloud-based AI platforms.

Nice-to-haves

  • Graduate degree in Statistics, Mathematics, Operations Research, Applied Economics, or related quantitative discipline.
  • Professional Data Science experience at a Marketing Analytics agency or Consulting company.

Benefits

  • Competitive Total Rewards package focusing on overall well-being.
  • Foundation of health benefits.
  • Retirement and financial wellbeing.
  • Time off programs.
  • Wellbeing support and perks.
  • Eligibility to participate in an annual incentive program.
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