Data Scientist

Blend is building a scalable Media Mix Optimization (MMO) solution designed to help clients maximize the impact of their marketing investments. We are seeking a Data Scientist with strong expertise in media mix modeling, statistical modeling, and interactive application development to join our advanced analytics team. This role goes beyond model building — you will design, implement, and productionize end-to-end solutions that integrate statistical rigor with business impact.

The ideal candidate will have deep knowledge of marketing analytics, advanced Python skills, and hands-on experience with Streamlit or similar frameworks for interactive data applications. You will be central in creating robust pipelines, experimentation frameworks, and client-facing tools that directly inform media allocation decisions.​​​​​​

Project Overview: Media Mix Optimization (MMO)

Our MMO platform is an in-house initiative designed to empower clients with data-driven decision-making in marketing strategy. By applying Bayesian and frequentist approaches to media mix modeling, we are able to quantify channel-level ROI, measure incrementality, and simulate outcomes under varying spend scenarios.

Key components of the project include:

• Data Integration: Combining client first-party, third-party, and campaign-level data across digital, offline, and emerging channels into a unified modeling framework.

• Model Development: Building and validating media mix models (MMM) using advanced statistical and machine learning techniques such as hierarchical Bayesian regression, regularized regression (Ridge/Lasso), and time-series modeling.

• Scenario Simulation: Enabling stakeholders to forecast outcomes under different budget allocations through simulation and optimization algorithms.

• Deployment & Visualization: Using Streamlit to build interactive, client-facing dashboards for model exploration, scenario planning, and actionable recommendation delivery.

• Scalability: Engineering the system to support multiple clients across industries with varying data volumes, refresh cycles, and modeling complexities.

  Responsibilities

• Develop, validate, and maintain media mix models to evaluate cross-channel marketing effectiveness and return on investment.

• Engineer and optimize end-to-end data pipelines for ingesting, cleaning, and structuring large, heterogeneous datasets from multiple marketing and business sources.

• Design, build, and deploy Streamlit-based interactive dashboards and applications for scenario testing, optimization, and reporting.

• Conduct exploratory data analysis (EDA) and advanced feature engineering to identify drivers of performance.

• Apply Bayesian methods, regularization, and time-series analysis to improve model accuracy, stability, and interpretability.

• Implement optimization and scenario-planning algorithms to recommend budget allocation strategies that maximize business outcomes.

• Collaborate closely with product, engineering, and client teams to align technical solutions with business objectives.

• Present insights and recommendations to senior stakeholders in both technical and non- technical language.

• Stay current with emerging tools, techniques, and best practices in media mix modeling, causal inference, and marketing science.

• Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, Applied Mathematics, or related field.

• Proven hands-on experience in media mix modeling, marketing analytics, or econometrics.

• Strong proficiency in Python and key data science libraries (pandas, NumPy, scikit-learn, statsmodels, PyMC or similar Bayesian frameworks).

• Experience with Streamlit or equivalent frameworks (Dash, Shiny) for building data- driven applications.

• Proficiency in SQL for querying, joining, and optimizing large-scale datasets.

• Solid foundation in statistical modeling, regression techniques, and machine learning.

• Strong problem-solving skills with the ability to structure ambiguous business problems

  into data-driven solutions.

• Excellent verbal and written communication skills to translate technical outputs into

  business decisions.

  Preferred Qualifications

• Experience with Bayesian hierarchical models, time-series decomposition, and marketing attribution approaches.

• Familiarity with cloud-based platforms (AWS, GCP, Azure) for data processing, model training, and deployment.

• Experience with data visualization tools beyond Streamlit (Tableau, Power BI, D3.js, Plotly).

• Exposure to big data ecosystems (Spark, Hadoop) for large-scale data processing.

• Knowledge of causal inference techniques (propensity scoring, uplift modeling, geo-

  experiments).