Our Data Scientists play a pivotal role in planning, designing, and delivering advanced analytics projects for our clients. Our goal is to leverage large sets of data to drive actionable client insights through building data-centric products.
As a Data Scientist Level I, you will be solving complex marketing and business challenges from optimizing the cross-channel media mix, conduct customer and market segmentation scoring, affinity (market-basket) analysis, improve customer loyalty through monitoring customer churn and cohort analysis, run multivariate experience and media-lift tests – all through accessing, integrating, manipulating, mining and modeling a wide array of media, business, and other types of data.
- Gain a thorough understanding, limits, and potential of the underlying mechanics of the marketing and business processes and generate hypotheses
- Use centralized data storage systems to ingest, access, and integrate data sources
- Conduct exploratory analysis, and test hypotheses using various quantitative methods
- Integrate domain knowledge and apply / test various ML and advanced analytics techniques to perform classification or prediction tasks
- Summarize, visualize, communicate, and document analytic concepts, processes and results for technical and non-technical audiences
- Partner with internal and external stakeholders to establish clear analytical targets, approaches, and timelines
- Develop processes and tools to monitor and analyze model performance and data accuracy
- Identify insights and opportunities for increased effectiveness and efficiency in current processes
- Share learnings, conduct research, and contribute to advancing the collective knowledge and skills of our Data Science practice
We’re looking for rigorous analytic training in a data science or analytics role, which typically includes:
- 4-6 years professional experience, with 1-3 years experience in a dedicated data science capacity
- A Bachelor’s or Master’s (preferred) degree in computer science, data science, operations research, statistics, applied mathematics, or a related quantitative field or or equivalent work experience such as, economics, engineering and physics
- Hands-on experience in successfully launching, planning, and executing data science projects on a small and large scale
- Hands-on experience running common statistical or machine learning procedures, such as descriptive statistics, hypothesis testing, dimension reduction, feature transformation, supervised / unsupervised learning, text mining, time-series methods, simulations, and optimization
- Hands-on experience using Python, R, SQL, and Google Cloud (GCP) related products
- Hands-on experience working with common ETL tools
- Adept to agile methodologies and well-versed in applying DevOps/MLOps methods to the construction of ML and data science pipelines.
- Marketing / agency experience strongly preferred
- Flexible and responsive able to perform in a fast paced, dynamic work environment and meet aggressive deadlines
- Ability to quickly adapt to new technologies, tools and techniques
- Ability to work with technical and non-technical team members