Data Scientist

The Data Scientist will contribute to the development of solutions for digital marketing use cases through statistical methods, data mining and data engineering, working closely with the Analytics, Media Account Management, and Technical Solutions Engineering teams.

They will be a data specialist with solid experience in the implementation of propensity models, segmentation analysis, churn modeling, recommendation systems, and descriptive analytics.

They will participate in all stages of research, development, and execution of projects for the assigned client, including project definition, data discovery, data engineering, model development and/or data mining, evaluating options, and making recommendations.

Responsibilities

Nurture client understanding of the importance of building & testing data-driven strategies.

Use predictive modeling to increase and optimize customer experiences, revenue generation, ad targeting and other business outcomes.

Use statistical analysis and machine learning libraries (R Stats, Python StatsModels, scikit-learn, etc.) to create models that quantify the influence of online activities on offline conversions.

Deploy cloud resources (Microsoft Azure, AWS, GCP or other) to perform analysis on large data sets.

Create ML pipelines models from data-wrangling to getting it into production.

Utilize data visualization techniques to explain data and models to clients and internal teams (Power BI, Tableau, Looker, etc.).

Mine data to support analytical projects and prepare data to support the development of information models to prove or disprove project hypotheses.

Design and manage experiments to ensure proper execution, data cleanliness, and statistical significance of results.

Act as a consultative resource to help clients understand the quality of their internal testing processes.

Participate in discovery workshops, feasibility analyses, project planning, estimations, and related activities.

Manage projects, including defining and overseeing deadlines, maintaining roadmaps, setting priorities, and ensuring the timely delivery of project milestones.

Qualifications

MSc or higher in a quantitative STEM subject such as Computer Science, Mathematics and Physics.

Proven experience articulating, translating, and solving business problems through data.

Hands-on experience in model deployment, governance, and workflow optimization (MLOps).

3+ years of experience in data science (statistical modeling, machine learning for forecasting, classification and optimization)

Predictive modeling

Experience analyzing large data sets with the Python or R data ecosystem.

Experience using large databases (SQL, Snowflake, BigQuery or similar).

Ability to explain the analytical methods and results to non-technical stakeholders to drive data-driven decision making.

Willingness to both teach others and learn new techniques.

Desirable qualifications

Experience with digital analytics platforms (Google/Adobe Analytics) and measurement solutions in the digital advertising industry, preferably with exposure to sectors such as retail, financial services/retail banking, insurance, telecommunications, automotive, or consumer packaged goods (CPG).

A set of certifications or work experience in cloud vendors (GCP, AWS, Azure).

Experience using Power BI, Tableau, Looker, or similar.