Senior Data Scientist, Customer Analytics (Remote)


Location: Burlington, Vermont

Type: Full Time

Education: Bachelor's Degree

Experience: 5 - 10 Years

IXIS is seeking an experienced data science practitioner with demonstrated technical leadership in the field of customer analytics to fill the position of Senior Data Scientist for the company. The core responsibilities for this role will be to apply and innovate quantitative methodologies that provide actionable insights into our clients’ customer journeys, including predictive customer lifetime value, churn modeling, segmentation and profiling, media attribution, and incrementality estimation. Experience working directly with consumer data, preferably in the retail sector, is required. You will be encouraged to develop solutions that can be scaled within our proprietary ATLAS data platform, and you will have the opportunity to contribute directly to our product roadmap by productizing innovations developed during client engagements. The ideal candidate will have multiple years of experience performing advanced customer analytics for top-tier businesses in omnichannel retail, CPG, automotive, or similar verticals.

This is a full-time, remote or hybrid position in our Burlington, Vermont or Washington, DC area office. We offer competitive compensation packages including health, dental, life, short-term and long-term disability and vision insurance, 401(k) with company match, flexible work schedules, and exceptional growth opportunities.


  • Plan, design and implement quantitative solutions for our B2C clients that drive actionable and ongoing insights, leveraging state of the art ML/AI technologies and libraries to solve real-world business problems
  • Build predictive models, derive media insights, define statistically sound solutions including but not limited to marketing mix models, multi touch attribution, uplift targeting, automated optimization strategies, and advertiser-facing recommendations
  • Provide technical leadership for advanced customer analytics initiatives, including predictive customer lifetime value, churn modeling, segmentation and profiling, media attribution, and incrementality estimation
  • Lead requirements gathering for advanced data science projects, oversee technical/design documentation, and communicate progress and results to non-technical audiences
  • Organize and oversee the progress of technical project teams, including other data scientists, data/ML engineers, and data analysts, as required to deliver larger quantitative solutions
  • Identify and prioritize opportunities for new data science products and ML/AI applications as offerings for our clients and/or as features within our ATLAS data platform
  • Use Python/R and a modern data science stack (dbt, AWS, Docker, Gitlab) to ensure that solutions are reproducible, extensible, and easily integrated into the ATLAS data platform
  • Lead by example: Foster a culture of curiosity, creativity, experimentation, and continual improvement among the data science team
  • Work with data/ML engineers and back-end engineers to deploy and maintain predictive models in an AWS production environment
  • Collaborate effectively across teams and departments, including strategists, account management, engineers, product, and design/UX
  • Prioritize multiple tasks intelligently and maintain clear lines of communication with supervisor and team

Required Skills and Experience

  • B.A./B.S. in a quantitative field (such as Statistics, Applied Math, Data Science or Economics); post-graduate degree preferred (commensurate professional experience will be considered)
  • 5+ years’ professional experience with programmatic data analysis, visualization, and modeling using Python/R + SQL; must be adept at functional programming and love writing clean code – exposure to DevOps and MLOps practices a plus
  • Professional experience with A/B testing, ideally in an ecommerce context, including design and analysis of controlled experiments
  • Expertise working with data in the wild, including structured and unstructured data, record linkage/householding, data munging/reshaping, descriptive and inferential statistics, and data visualization for exploratory and presentation purposes
  • Demonstrated success developing and deploying ML pipelines for business applications
  • Fluency with marketing fundamentals (up to and including MMM) and digital analytics (Google/Adobe Analytics) a big plus
  • Uncompromising attention to detail, critical thinking skills, and a systematic approach to problem-solving, with strong data intuition

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