Staff Data Scientist

Key Responsibilities:

• Serve as the technical lead on complex client engagements, owning analytical strategy, modeling approach, and solution quality.
• Partner closely with client stakeholders, analytics leads, and consulting teams to translate business questions into robust analytical solutions.
• Guide clients on measurement strategy, model interpretation, and decision implications, not just outputs.
• Influence analytical direction across multiple engagements through best practices, frameworks, and technical guidance.
• Design, develop, and validate advanced analytical models across use cases such as:
• Media Mix Modeling (MMM)
• Multi-Touch Attribution (MTA)
• Causal inference, forecasting, segmentation, and optimization
• Apply Bayesian and probabilistic modeling techniques (e.g., PyMC, Meridian) wherever appropriate to the business context.
• Lead feature engineering, model evaluation, and sensitivity analysis using real-world, imperfect data.
• Make informed trade-offs between model sophistication, interpretability, and actionability for client decision-making.
• Ensure models are client-ready, explainable, well-documented, and production-aware.
• Collaborate with data engineering teams to support deployment, monitoring, and iteration of models.
• Create reusable analytical components, accelerators, and methodologies to improve delivery efficiency and consistency.
• Review and elevate the quality of modeling work across the team through technical reviews and mentorship.

Required Skills:

• 812 years of experience in applied data science or advanced analytics, preferably in consulting, marketing analytics, or client-facing roles.
• Deep hands-on experience with Media Mix Modeling (MMM) and Multi-Touch Attribution (MTA), including Lightweight MMM and Meridian-based approaches.
• Strong expertise in Bayesian modeling and statistical inference (e.g., PyMC).
• Advanced proficiency in Python and/or R, with experience using ML libraries (scikit-learn, XGBoost; deep learning as applicable).
• Experience building models such as:
• Forecasting and response/scoring models
• Segmentation and optimization models
• Causal inference frameworks
• Strong ability to communicate complex analytical concepts clearly to senior, non-technical stakeholders.
• Proven track record of leading analytics projects end-to-end with real business impact.

Desired Skills:

• Exposure to NLP or text analytics (topic modeling, embeddings).
• Familiarity with LLM-based applications and applied AI use cases.
• Experience with optimization, simulation, or reinforcement learning where relevant.
• Working knowledge of cloud platforms (AWS) and production ML workflows.
• Familiarity with digital marketing analytics, including paid media, SEO, and search-related modeling.

Qualifications:

• Master’s or PhD in a quantitative field (Computer Science, Statistics, Applied Mathematics, Data Science, Operations Research, Economics, Engineering).
• 8–12 years of experience in applied data science/modeling, ideally with projects spanning MMM, MTA, predictive modeling, NLP, optimization, and business-focused analytics.
• Experience delivering models into production environments (not just research/prototyping).