Job Description

Havas CSA is seeking a Data Scientist with 2-4 years of experience to contribute to advanced analytics and predictive modelling initiatives. The ideal candidate will combine strong statistical knowledge with practical business understanding to help develop and implement models that drive customer value and business growth.

Responsibilities:

Implement and maintain customer analytics models including CLTV prediction, propensity modelling, and churn prediction Support the development of customer segmentation models using clustering techniques and behavioural analysis Assist in building and maintaining survival models to analyze customer lifecycle events Work with large-scale datasets using BigQuery and Snowflake Develop and validate machine learning models using Python and cloud-based ML platforms, specifically BQ ML, ModelGarden and Amazon Bedrock Help transform model insights into actionable business recommendations Collaborate with analytics and activation teams to implement model outputs Present analyses to stakeholders in clear, actionable formats

Qualifications:

Bachelor's or master's degree in Statistics, Mathematics, Computer Science, or related quantitative field 1-2 years' experience in applied data science, preferably in marketing/retail Experience in developing and implementing machine learning models Strong understanding of statistical concepts and experimental design Ability to communicate technical concepts to non-technical audiences Familiarity with agile development methodologies

Technical Skills:

Advanced proficiency in:

SQL and data warehouses (BigQuery, Snowflake) Python for statistical modeling Machine learning frameworks (scikit-learn, TensorFlow) Statistical analysis and hypothesis testing Data visualization tools (Matplotlib, Seaborn) Version control systems (Git) Understanding of Google Cloud Function and Cloud Run

Experience with:

Customer lifetime value modeling RFM analysis and customer segmentation Survival analysis and hazard modeling A/B testing and causal inference Feature engineering and selection Model validation and monitoring Cloud computing platforms (GCP/AWS/Azure)

Key Projects & Deliverables

Support development and maintenance of CLTV models Contribute to customer segmentation models incorporating behavioral and transactional data Implement survival models to predict customer churn Support the development of attribution models for marketing effectiveness Help develop recommendation engines for personalized customer experiences Assist in creating automated reporting and monitoring systems

Soft Skills

Strong analytical and problem-solving abilities Good communication and presentation skills Business acumen Collaborative team player Strong organizational skills Ability to translate business problems into analytical solutions

Growth Opportunities

Work on innovative data science projects for major brands Develop expertise in cutting-edge ML technologies Learn from experienced data science leaders Contribute to impactful analytical solutions Opportunity for career advancement
We offer competitive compensation, comprehensive benefits, and the opportunity to work with leading brands while solving complex analytical challenges. Join our team to grow your career while making a significant impact through data-driven decision making.

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.

Job Type: Full-time

Pay: ?1,000,000.00 - ?1,300,000.00 per year

Benefits:

Health insurance Life insurance Paid sick time Paid time off Provident Fund Work from home
Ability to commute/relocate:

Andheri, Mumbai, Maharashtra: Reliably commute or planning to relocate before starting work (Preferred)
Experience:

Data science: 2 years (Preferred)
Work Location: In person