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Popular mid-level data scientist title, metro location, and 3-6 years experience increase candidate competition.
Role demands domain-specific pricing, MMM, and causal inference experience, so industry fit sensitivity is high.
Explicit 4+ years plus many mandatory technical, statistical, and domain-specific skills makes shortlisting strict.
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Lead, mentor, and provide technical direction to data scientists across multiple projects in customer and marketing analytics.
Own end-to-end delivery of advanced data science solutions including pricing optimization, customer segmentation, predictive modeling, Bayesian methods, causal inference, and optimization.
Engage with clients and senior stakeholders to translate complex analytical insights into actionable business recommendations and measurable outcomes.
4+ years of hands-on experience in Data Science or Advanced Analytics with leadership or mentoring experience.
Strong programming skills in Python (pandas, NumPy, scikit-learn) and SQL (including joins, window functions, aggregations).
Hands-on experience with Bayesian modeling, pricing optimization, machine learning techniques (K-Means, GMM, DBSCAN, Random Forest, SVM), causal inference, and AWS SageMaker deployment.
Master's degree in quantitative discipline (Data Science, Statistics, Mathematics, Economics, Computer Science, Engineering) preferred; relevant domain experience in financial services, retail, media, marketing, or customer analytics preferred.
Experienced leader comfortable balancing hands-on technical delivery with mentoring and client engagement in data science projects.
Strong expertise in advanced statistical methods, machine learning, Bayesian modeling, causal inference, and optimization focused on pricing and marketing analytics.
Capable of driving business impact by translating complex models and experiment results into actionable insights for revenue and marketing decisions.