Senior Data Scientist with MMM

Role : Senior Data Scientist with MMM

Not sure what skills you will need for this opportunity Simply read the full description below to get a complete picture of candidate requirements.

Work Location : San Jose, CA - Onsite from day 1

Experience : 8+ yrs Exp.

Duration : 12+ Months

Technical Skills

• Machine Learning : SciPy, Sckit-learn, Pandas
• Technique : Hyperparameter Tuning, Imbalanced Data, Ensemble Learning, Boosting
• Algo : SVG, XGBoost, KNN, Clustering, Views, MassTer, Stata etc.
• Natural Language Processing : LLM familiarity (BERT), Neural Networks (LSTM)
• Big Data Experience : Cloud Services (GCP, Microsoft Azure, AWS)
• Expert knowledge on Time series Forecasting, Python Libraries
• 6+ years of experience in MMM, measurement, A / B testing, marketing strategy & analytics
• Proficient coding skills (Python / SQL) and database knowledge
• Experience with advertising technology platforms Ad servers, DSPs, DMPs, etc.
• Proficient in SQL and Python Libraries (Tensor Flow, Keras, PyTorch)

Domain Knowledge

Candidate needs to have extensively exposure and hands-on knowledge on Market Mix Modelling (MMM) and should possess strong Marketing Domain Knowledge.

Responsibilities

• Conducting the appropriate analytics techniques on available data. This will focus on Market Mix Modelling but will also include a wider range of techniques such as portfolio management and digital analytics.
• Deriving meaningful insights from the results of a project.
• Running and delivering practical and implementable media optimization scenarios supporting the decision-making process for the planning teams.
• Communicating results to internal teams and establishing buy-in of recommendations.
• Working within a team of 10-13 people across different teams to ensure project deliverables are produced on time.
• This is a hands-on role : Collecting data from relevant sources, checking for accuracy and formatting for analytics.
• Developing strong working relationships with the account planning teams and fostering a culture of information sharing.
• Showcasing Media Mix Modelling and its benefits to planning teams to create agency-wide awareness.
• Learning and implementing optimization tools in cooperation with the wider data science team.
• Building and enhancing media mix models to connect the impact of marketing drivers and business short-term and long-term outcomes.
• Developing optimization and simulation algorithms to guide the marketing investment and allocation recommendations to stakeholders.
• Scaling current Python modeling algorithm through parallel processing or other efficiency enhancements.
• Designing and executing A / B tests and other experiments.
• Explaining complex modeling approaches in simple terms and developing compelling narratives that connect modeling results with business problems.
• Partnering with cross-functional teams to streamline data science solutions.

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