[About the job]
Our team is looking for experienced data scientists to build models for marketing AI. On the 8ndpoint team, you’ll have the opportunity to build out our core product: machine learning models that deliver insights and value to our customers. As a Data Scientist, you’ll be responsible for model R&D, feature engineering, productionizing models, and identifying and exploring new modeling opportunities as the business grows. You’ll have the full support of data science and engineering teams to help deliver performant models, and you’ll also work closely with Product to deliver the power of AI to our customers.
MoBagel has an extraordinarily open and relaxed work culture. There’s immense freedom to work on what you think is most important, and generous support for personal development. We’re a small and rapidly growing team with great work-life balance, generous remote work policy, open and supportive teammates, and free food. Come meet the team!
Since this role is highly cross-functional, there are ample opportunities (and support) for diving into DevOps, MLOps, data engineering, and machine learning systems projects.
1. Research, prototype, build, and maintain machine learning models to solve business problems.
2. Launch models to production and support machine learning platform development.
3. Perform exploratory analyses to scope high-value opportunities.
4. Build and maintain complex Airflow DAGs or equivalent to meet changing modeling, product, and engineering requirements.
5. Adhere to best software development practices.
6. Write technical documentation and share technical knowledge and decisions with the team.
1. 3+ years professional experience in a technical or product-oriented role.
2. 2+ years professional experience in predictive modeling using large and complex datasets.
3. Strong machine learning fundamentals and demonstrated experience with different machine learning methods in production (e.g. regression, trees, clustering, deep learning, etc).
4. Strong technical communication skills and demonstrated experience working cross-functionally across different teams and functions.
5. Experience with SQL and data orchestration tools such as Airflow or Prefect.
6. Proven ability to scope and solve problems both independently and with a product team.
7. A creative spark for problem solving and finding new ways to improve model performance.
[Nice to Have]
1. Strong Computer Science fundamentals, including data structures and algorithms.
2. Experience on a data or machine learning team in a production environment.
3. Familiarity with distributed data systems fundamentals.
We prioritize candidates that can quickly learn new technologies over domain knowledge, but the following is a snapshot of what our day-to-day looks like:
1. Development: Python, Java, Scala
2. Machine Learning: Python (Pandas, sklearn, xgboost, etc.)
3. Orchestration: Airflow, Kubernetes, Docker
4. GCP: BigQuery, GKE, Artifact Registry, Cloud SQL, Pub/Sub
5. Frontend: React, TypeScript
6. DevOps: ArgoCD, Rancher/Longhorn
7. VCS & CI/CD: Gitlab