Job description
We are seeking a Senior Data Scientist to join our fast-growing analytics team in New York City. You will build and deploy end-to-end ML models, collaborate with product, data engineering, and business stakeholders to deliver measurable impact. Our ideal candidate combines deep statistical expertise with practical production experience and a passion for turning data into strategic decisions.
At Northstar Analytics, you will work on challenging problems across retail, finance, and consumer tech, leveraging modern ML techniques to drive experimentation and impact. We value collaboration, curiosity, and a bias for action.
Responsibility
- Lead the design, development, and deployment of end-to-end machine learning models from problem framing to production.
- Collaborate with product, data engineering, and analytics teams to translate business needs into data-driven solutions.
- Develop feature engineering pipelines and scalable data processing architectures on cloud platforms.
- Evaluate and monitor model performance, perform error analysis, and implement robust governance and validation.
- Own experimentation lifecycle including A/B testing, experimentation design, and documentation.
- Communicate insights and recommendations to stakeholders with clear data storytelling and visualization.
- Mentor junior data scientists and contribute to code reviews and best practices.
- Stay current with advances in ML techniques and tooling to continuously improve our data products.
Qualification
- Master's or PhD in Data Science, Computer Science, Statistics, or a related field; or equivalent industry experience.
- 5+ years of hands-on experience building production ML models and data pipelines.
- Strong proficiency in Python or R, with experience in SQL and data processing frameworks (Spark, pandas).
- Experience with ML libraries (scikit-learn, TensorFlow, PyTorch) and ML model deployment in cloud environments (AWS, GCP, Azure).
- Proven ability to translate business problems into measurable data products and present findings to non-technical audiences.
- Experience with experimentation, causal inference, and model monitoring / governance.
- Excellent problem-solving, communication, and collaboration skills; ability to work in an agile environment.
- Familiarity with data visualization tools (Tableau, Looker, Power BI) and data storytelling.