ABOUT THE ROLE
You will own the technical architecture of Acxiom's identity graph in MENAT and lead the data science team that works on marketing mix modelling (MMM) and High-Value Audience (HVA) solutions. The role sits between data engineering, data science and leadership. You design how identity data is resolved, governed and activated, then prove its commercial impact to clients.
KEY RESPONSIBILITIES
ID graph architecture
- Own the end-to-end design of the ID graph: ingestion, deterministic and probabilistic matching, identity resolution logic, match-rate and precision measurement, and refresh cadence.
- Define the data model, pipelines and cloud architecture (BigQuery, Snowflake) with data engineers; set standards for scalability, cost and latency.
- Build privacy-by-design into the graph: consent handling, anonymization/pseudonymization, clean-room integrations, and compliance with UAE PDPL, KSA PDPL and client policies.
- Evaluate and onboard data partners and match keys; decide build vs. partner vs. buy.
Data science leadership (MMM & HVA)
- Lead delivery of MMM and multi-touch attribution work, including Bayesian MMM, calibration with incrementality tests, and budget optimization.
- Lead High-Value Audience modelling: propensity, lookalike, CLV and segmentation models activated through the ID graph.
- Set the team's methodology, code quality and model validation standards; move repeat work into reusable products.
People & stakeholders
- Hire, coach and manage a team of data scientists; set goals and run performance reviews.
- Partner with client services, sales and product to scope solutions and support pre-sales.
- Present architecture decisions and model results to clients, the MD and the CEO in clear, decision-ready decks.
MUST-HAVE QUALIFICATIONS
- 10+ years in data science, data products or AI, including 3+ years leading teams.
- Hands-on depth in statistical and machine learning models: regression, Bayesian methods, time series, classification, clustering.
- Strong coding in SQL and Python; working knowledge of R.
- Production experience in big data environments such as BigQuery or Snowflake.
- Hands-on experience with identity resolution, customer data platforms or first-party data matching.
- Track record of working with data engineering and tech leads to ship data products.
- Proven delivery in media measurement: MMM, multi-touch attribution or incrementality testing.
- Strong media domain background: agency, adtech or data-provider experience
- PowerPoint narratives for C-level audiences.
