Director, Digital Data Science in Analytics & Insights - Direct Hire (100% On-Site w/some flex)
- Job Title
- Director, Digital Data Science in Analytics & Insights - Direct Hire (100% On-Site w/some flex)
- Job ID
- 27705621
- Location
- Ann Arbor, MI 48106
- Other Location
- Description
-
Director, Digital Data Science in Analytics & Insights
Ann Arbor, MI (100% On-Site with some flex)
$180,000/yr. - $200,000/yr. – Full-time Director Level
20% Bonus
30% Stock Incentive
Our History:
From our start in 2009, Conexess has established itself in 3 markets, employing nearly 200+ individuals nation-wide. Operating in over 15 states, our client base ranges from Fortune 500/1000 companies, to mid-small range companies. For the majority of the mid-small range companies, we are exclusively used due to our outstanding staffing track record
Who We Are:
Conexess is a full-service staffing firm offering contract, contract-to hire, and direct placements. We have a wide range of recruiting capabilities extending from help desk technicians to CIOs. We are also capable of offering project based work.
The Director, Digital Data Science in Analytics & Insights will accelerate our competitive advantage in Digital Transformation (DX) and Customer Experience (CX) Analytics by helping to shape and lead its vision and roadmap. We are seeking an influential and collaborative leader with expertise in measuring and maximizing our customer lifetime value plus igniting sustained growth across various digital customer touchpoints. Based in Ann Arbor, MI, this exciting role will lead a team of 8 Data Scientists (including a Senior Manager plus a Manager), and will report to the VP, Analytics and Insights. You will partner with Marketing and IT teams to improve the DX/CX and oversee a team to continue advancing personalized, multi-variate testing and sophisticated web analytics approaches utilizing the vast customer data within our web logs, proprietary customer database, and other data sources.
Responsibilities- Steer customer-centric strategies that improve our customer lifetime value
- Shape and track progress towards our personalization milestones as well as outline the data and analytics roadmap and investments required to enable these efforts
- Architect a growth roadmap that scales user acquisition and lifecycle marketing through a multi-channel strategy (e.g., paid media, social paid/earned, loyalty, community/influencer development, targeted offers) for a more personalized approach to CRM
- Steer the analytics driving our loyalty program evolution to ensure continued membership growth, incrementality, and profitability
- Identify opportunities for optimization along the digital customer journey, unlocking ideas for a more frictionless experience and improved conversions
- Ensure best-in-class approaches in Digital Marketing Analytics, Customer Lifetime Value, CRM, A/B testing, and Targeting
Knowledge, Skills, and Abilities- 10+ years of progressive experience in data science, analytics, or related field
- 5+ years of management experience
- Experience in personalization as well as with customer data to identify new ways of leveraging existing data and ways to modernize current approaches
- Good understanding of the QSR business and/or experience in the travel, ecommerce, or entertainment industries are a plus
- Bachelor’s Degree in business, statistics, computer science, or related field. Master’s preferred
- Proven track record of effectively managing change in a highly collaborative/fast-paced setting
- Excellent verbal, written, and presentation communication skills across levels and functions
- Results-driven, identifying and prioritizing activities that drive impact
- Demonstrated effective problem-solving skills
- Encourages diversity, curiosity, and explores new ways of doing things
- Effectively coaches, inspires, and develops team, providing stretch opportunities plus building strong engagement and performance
- Leverages complex data models to tell a persuasive and simple story
- Experience with analytic tools such as Adobe Analytics, Google Analytics, SQL, R, Python, Alteryx, Tableau, as well as very large data sources, and applied statistics. This includes causal modeling, time series analysis, and data-mining techniques in collecting and predicting consumer sentiment, behavior, and feedback.
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