We are looking for a Manager Data Science to continue our success of identifying high quality target audiences that generate profitable marketing return for our clients. We are looking for experienced data science, machine learning and MLOps practitioners to design, build and deploy impactful predictive marketing solutions that serve a wide range of verticals and clients. The right candidate will enjoy contributing to and learning from a highly talented team and working on a variety of projects.

Key Responsibilities:

1. Ownership of design, implementation, and deployment of machine learning algorithms in a modern Python-based cloud architecture
2. Design or enhance ML workflows for data ingestion, model design, model inference and scoring
3. Oversight on team project execution and delivery
4. Establish peer review guidelines for high quality coding to help develop junior team members’ skill set growth, cross-training, and team efficiencies
5. Visualize and publish model performance results and insights to internal and external audiences

Qualifications:

1. Masters in a relevant quantitative, applied field (Statistics, Econometrics, Computer Science, Mathematics, Engineering)
2. Minimum of 12-15 years of work experience in the end-to-end lifecycle of ML model development and deployment into production within a cloud infrastructure (Databricks is highly preferred)
3. Proven ability to manage the output of a small team in a fast-paced environment and to lead by example in the fulfilment of client requests
4. Exhibit deep knowledge of core mathematical principles relating to data science and machine learning (ML Theory + Best Practices, Feature Engineering and Selection, Supervised and Unsupervised ML, A/B Testing etc.)
5. Proficiency in Python and SQL required; PySpark/Spark experience a plus
6. Ability to conduct a productive peer review and proper code structure in Github
7. Proven experience developing, testing, and deploying various ML algorithms (neural networks, XGBoost, Bayes, and the like)
8. Working knowledge of modern CI/CD methods