Mars United℠ Commerce is a global commerce marketing practice that aligns people, technology, and intelligence to make the business of our clients better today than it was yesterday. Our worldwide capabilities coalesce into four key disciplines — Strategy & Analytics, Content & Experiences, Digital Commerce, and Retail Consultancy — that individually deliver unmatched results for clients and collectively give them an unparalleled network of seamlessly integrated functions across the entire commerce marketing ecosystem. These disciplines are powered by our industry-leading technology platform, Marilyn®, which helps marketers understand the total business impact of their commerce marketing activation, enabling them to make better decisions, create connected experiences, and drive stronger, measurable results. Learn more at https://www.marsunited.com/.

Part of the overall Analytics Group, the Data Science team is responsible for all data modeling, algorithm development, and creating machine learning and AI models. They develop techniques such as regression, classification, clustering, natural language processing (NLP), and more. Additionally, they focus on marketing analytics, using advanced data science techniques to analyze marketing performance, optimize campaigns, and provide actionable insights to enhance marketing effectiveness.

● Core Responsibilities: Data Modeling, Feature Engineering, Sentiment Analysis, Propensity Modeling, CM3, Model Training & Testing, Forecasting, Multi-touch/Data-driven Attribution, etc.

● Primary Tools: Databricks, Azure Synapse, Alteryx, Python, SQL

As a Data Scientist, you will leverage your strong technical skills and experience to develop data science and AI solutions. This role requires a deep understanding of data science and machine learning techniques and the ability to collaborate with various teams to ensure data quality and actionable insights. Specifically, the Data Scientist will:

● Collaborate with stakeholders to understand business requirements and translate them into actionable data science projects.

● Work closely with cross-functional teams, including analysts, product managers, and domain experts, to understand business requirements, formulate problem statements, and deliver relevant data science solutions.

● Develop and optimize machine learning models by processing, analyzing and extracting data from varying internal and external data sources.

● Develop supervised, unsupervised, and semi-supervised machine learning models using state-of-the-art techniques to solve client problems.

● Own and manage complex ETL pipelines to clean, preprocess, and transform large datasets.

● Identify and engineer relevant features to enhance model performance and accuracy.

● Design and implement robust evaluation metrics and frameworks to assess and monitor the performance of machine learning models.

● Communicate findings and recommendations through comprehensive reports and engaging presentations.

● Support wider agency initiatives.

● Show up - be accountable, take responsibility, and get back up when you are down.

● Make stuff.

● Share so others can see what’s happening.

A Bachelors’/Master’s degree in Mathematics, Statistics, Data Analytics, Computer Science, or a directly related field.

● 1+ years of industry experience in a data science/data analysis/statistical analyst role.

● Comfortable in manipulating and analyzing complex, high-volume, high-dimensionality data from varying sources using Python/R libraries and SQL.

● Familiarity with relational, SQL, and NoSQL databases.

● Databricks experience is a big plus point.

● Knowledge of statistical analysis tools such as R is a plus.

● Knowledge of scripting in SQL and Python using OOP concepts.

● Experience with PowerBI or Tableau.

● Experience with cloud platforms (e.g., AWS, Azure, Google Cloud) and big data technologies.

● Experience in DevOps or MLOps is a plus.

● Experience with DS or ML frameworks and libraries (e.g., Spark, TensorFlow, PyTorch) is a plus.

● Strong communication skills to effectively convey complex findings to non-technical stakeholders.

● Collaborative mindset to work seamlessly with creative, strategic, and client-facing teams.

● Critical thinking to analyze data and derive meaningful insights.

● Experience in the marketing domain is preferred.

● Ensure the accuracy and reliability of data through rigorous QA processes.

● Validate model outputs to ensure they meet business requirements.

● Conduct unit tests and validation checks on data and models.

● Perform A/B testing to evaluate model performance and impact.

● Document all data analysis and modeling processes.

● Maintain comprehensive records of data sources, methodologies, and results.

● Ensure compliance with data governance and security policies.