IQ Workforce is a leading recruiting firm for the engineering, analytics, and data science communities.

Our client is a leading restaurant brand with more than 2,300 bakery-cafes in the United States and Canada, 140,000 associates and annual systemwide sales in the billions. They are currently making a massive investment in technology and talent to support their marketing and customer focused initiatives.

One of the immediate needs is a Data Science Lead (Sr. Manager level) that will work closely with the Digital and Loyalty/CRM teams to understand problems and opportunities, leveraging marketing domain, applied statistics, and machine learning/engineering expertise to determine the right solutions, products, or data assets to create a long-term roadmap and near-term project plan for executing those deliverables.

The Data Science Lead will be a coach/player – managing a team of analysts and data scientists (4 FTEs and 5 full time contractors) while also exhibiting hands-on statistical and technical skills needed for diagnostic, descriptive, predictive models and experimental designs in support of the business, delivering findings, and presenting to users of all technical backgrounds. The right candidate for this role is passionate about driving improvement and possesses technical knowledge of data, customer analytics, research methodology, and can apply these skills in digital, marketing, and loyalty domains. This person will lead and guide data science initiatives, partner with internal and external analytics partners, and contribute to insight creation and projects.

Responsibilities include:
Understand organizational initiatives, translating to long-term planning and enablement of new data science capabilities and practices to support

Lead quarterly and monthly strategic prioritization cadence to ensure alignment of resources to initiatives, as well as weekly engagement with business stakeholders to prioritize, refine/groom, and communicate status updates on projects

Enable business operations and strategy with descriptive, predictive, and prescriptive modeled data assets

Selecting and applying statistical methods to create insight. Methods can include clustering, predictive modeling, market basket analysis, regression analysis, explanatory analysis, descriptive analysis, or any number of techniques to create insight

Application of research methodology in statistical applications to develop robust analyses to understand the customer-base better

Enhance analytical approaches and frameworks in coordination with digital marketing team members, data scientists, and digital analysts

Provide Marketing business-area direction to the Data Science, Guest Insights teams, and analytics vendors/partners to understand loyalty program trends and impact on data, analytics design, and findings

Answer crucial business questions with diagnostic and descriptive ad-hoc analyses

Encourage adoption of data assets through effective communication/presentation to help educate stakeholders on activation and practical business application, guiding them in the proper interpretation of the results.

Lead the cross-functional team by fostering and curating a culture that embraces data and data literacy; be a teacher, mentor, and guide.

Responsible for experimentation and experimental design to test and measure effectiveness across an array of channel media and treatment protocols

Help build the data science team and lead the growth of the role data science plays within the brand; responsible for hiring, team building, team evolution, and training

Qualifications:
Master’s Degree in Information Technology, Mathematics, Statistics or Marketing is required; or credentials within related data/analytical fields; Ph.D. preferred.

5-10 years of progressive experience working with customer and/or transactional data in advanced analytics, data science, or statistics function. Experience working with marketing, digital or loyalty data on multi-channel and one-to-one communication approaches preferred

Hands-on experience developing, training, validating, and tuning various ML models—decision trees/random forest, regression, SVM, TensorFlow, deep learning/neural nets, boosting/bagging/stacking, clustering, and advanced causal inference (e.g. synthetic controls, bootstrapping, forecast/simulation).

Strong expertise with data analysis/ML tools and platforms such as Python (pandas, scikit-learn), SAS, SPSS, R, Vertex AI

Strong skills within data query tools such as SQL and warehousing technologies such as Oracle and Hadoop, as well as experience with Google Cloud Platform Big Query a plus.

Ability to understand business objectives and requirements, organize and interpret research results, and deliver insights and recommendations in the marketing domain
 
Impeccable written and verbal communication skills, an expert in presentation and data visualization/story-telling