AdTechTalent
Data Science42 days agoOn-site

Samba TV

Data Scientist

pythonpysparkdatabricksmachine learningbayesian inferencecausal mlmulti-touch attributionuplift modelingstatistical modelingdata scienceadvertising measurementviewership data

Key details

Salary

$150K – $180K

Employment type

Full-time

Seniority

Senior

Years experience

5-10

Location

San Francisco, California, United States

Full job description

Samba is seeking a senior Data Scientist to develop and deliver complex measurement science and modeling for audience science products. The role requires deep understanding of data science and machine learning principles, with emphasis on writing production-quality Python and PySpark code for large-scale datasets. Responsibilities include building measurement models, applying statistical and ML techniques, developing multi-touch attribution models, collaborating with Data Engineering, leading design reviews, mentoring junior scientists, and communicating findings to stakeholders. Candidates should have 5-7 years of professional data science experience, expert Python skills, advanced PySpark and Databricks experience, strong statistical and experimental design knowledge, hands-on causal ML and attribution modeling experience, exposure to TV/digital viewership data, and an advanced quantitative degree or equivalent experience. Benefits include health insurance, wellness programs, life and disability insurance, retirement plans, paid holidays and PTO, and performance incentives. The position is based in San Francisco, California with a salary range of $150,000 to $180,000 USD per year.

What you'll do

  • Write and own production-quality Python code end-to-end, including PySpark for large datasets
  • Design, build, and deploy measurement models and statistical frameworks for campaign measurement, reach/frequency estimation, and cross-platform attribution
  • Apply appropriate statistical and ML techniques and articulate reasoning behind choices
  • Build and evaluate multi-touch and multi-channel attribution models using Causal ML methods
  • Partner with Data Engineering to define data requirements, validate pipelines, and ensure production readiness
  • Lead technical design reviews and contribute to architecture decisions within the Data Science team
  • Mentor junior Data Scientists through code review, pairing, and technical feedback
  • Communicate measurement methodologies and findings clearly to technical and non-technical audiences including senior leadership and external clients

Requirements

  • 5-7 years of professional data science experience with shipped models and production systems
  • Expert-level Python coding skills producing clean, modular, testable, production-ready code
  • Advanced PySpark and Databricks experience with billion-row datasets
  • Deep understanding of statistics and machine learning from first principles
  • Solid grasp of experimental design including A/B testing, randomization, power analysis, and observational causal inference
  • Fluent in full ML lifecycle: feature engineering, model evaluation, deployment pipelines, drift monitoring, iterative improvement
  • Hands-on experience with uplift modeling, synthetic control, difference-in-differences, or propensity-based approaches in advertising or media
  • Strong ownership mindset with ability to independently drive projects from data exploration to production
  • Clear communication skills to translate statistical reasoning and model behavior to technical and non-technical stakeholders
  • Experience with multi-touch attribution (MTA) or multi-channel attribution modeling
  • Hands-on experience with Causal ML methods applied to advertising or media measurement
  • Direct exposure to TV or digital viewership data such as ACR signals, STB data, viewership panels, or cross-platform measurement
  • Familiarity with measurement vendor landscape (Nielsen, Comscore, VideoAmp, iSpot) and industry standards (MRC, GRP/TRP frameworks)
  • Advanced degree (MS or PhD) in Statistics, Mathematics, Computer Science, or related quantitative field or equivalent experience

Tech stack

PythonPySparkDatabricksMachine LearningBayesian inferenceGradient boostingRegularized regressionCausal MLProbabilistic record linkageMeta-learners (S-learner, T-learner, X-learner)Uplift modelingSynthetic controlDifference-in-differencesPropensity-based approaches

Benefits

Health insuranceWellness offeringsLife and disability insuranceRetirement savings planPaid holidaysPaid time off (PTO)Bonuses, short-term incentives, and long-term incentives

Apply now

This MVP uses a placeholder application flow. In production, this section can connect to an external apply URL or a native application form.

Continue to applicationBrowse more jobs

Similar jobs

More roles worth a look

Related opportunities based on specialty and working model so candidates can keep momentum.