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Havas Chicago

Data Scientist- CSA

Posted 2 Months Ago
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In-Office
Mumbai, Maharashtra, IND
Mid level
In-Office
Mumbai, Maharashtra, IND
Mid level
Build and productionize lead scoring, churn, and media-mix models; write SQL and Python pipelines; use GCP/BigQuery; translate model outputs into actionable marketing recommendations and work with cross-functional teams to embed analytics into campaigns.
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Agency :

PivotRoots

Job Description :

Havas CSA is looking for a Data Scientist to join our analytics team, working on marketing and media analytics problems across FMCG, BFSI, retail, and automotive clients. The role blends hands-on statistical/ML modeling with a strong understanding of media and marketing performance and requires the ability to translate business questions into data solutions that clients can act on.

  Key Responsibilities:

  • Build and maintain lead scoring models to prioritize high-intent customers/prospects for sales and marketing teams

  • Develop churn prediction models to identify at-risk customers and support retention strategies

  • Design and execute Media Mix Modeling (MMM) studies to measure channel-level marketing effectiveness and guide budget allocation

  • Write efficient SQL queries to extract, transform, and validate data from large marketing and transactional datasets

  • Build and productionize data pipelines and models in Python (pandas, scikit-learn, statsmodels, or equivalent)

  • Work within Google Cloud Platform (GCP) — BigQuery, Cloud Run/Functions, IAM, and related services — to build scalable data workflows

  • Translate model outputs into clear, business-relevant recommendations for marketing and media teams

  • Collaborate with client servicing, media planning, and analytics teams to embed data science outputs into campaign and business decisions

  • Stay current on marketing measurement approaches (attribution, incrementality, media effectiveness) and apply them to client problems
     

Required Skills & Experience:

  • Strong proficiency in Python for data analysis and machine learning

  • Solid SQL skills, comfortable working with large, complex datasets

  • Working knowledge of GCP, particularly BigQuery; familiarity with cloud-based data pipelines

  • Practical experience building lead scoring and churn prediction models

  • Experience with or strong conceptual understanding of Media Mix Modeling (e.g., adstock, saturation curves, Bayesian approaches such as Meridian, Robyn, or PyMC-Marketing is a plus)

  • Good understanding of media and marketing concepts — campaigns, channels, funnels, attribution, and marketing KPIs

  • Ability to communicate technical findings to non-technical stakeholders, including clients

  • Willingness to build expertise in Google Ads Data Hub (ADH), or prior exposure to ADH study types (Frequency Analysis, Brand Lift, Path to Conversion, etc.)
     

Good to Have:

  • Experience with GA4, Google Ads, Meta Ads, or other marketing platform data

  • Exposure to reverse ETL / audience activation workflows

  • Experience with Looker Studio, Power BI, or similar BI tools

  • Prior experience in a media agency, ad-tech, or martech environment
     

Qualifications:

  • Bachelor's/Master's degree in Statistics, Computer Science, Data Science, Economics, or a related quantitative field

  • 2–5 years of relevant experience in data science/analytics roles (adjust per seniority)
     

Here at Havas across the group we pride ourselves on being committed to offering equal opportunities to all potential employees and have zero tolerance for discrimination. We are an equal opportunity employer and welcome applicants irrespective of age, sex, race, ethnicity, disability and other factors that have no bearing on an individual’s ability to perform their job

Contract Type :

Permanent

Here at Havas across the group we pride ourselves on being committed to offering equal opportunities to all potential employees and have zero tolerance for discrimination. We are an equal opportunity employer and welcome applicants irrespective of age, sex, race, ethnicity, disability and other factors that have no bearing on an individual’s ability to perform their job.

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