Machine Learning Engineer

Machine Learning Engineer

Location: Long Island City, NY 11101 (Onsite 4 Days/week)

Type: Permanent Full Time

About the Role:

In this role, you will take the lead in developing and fine-tuning predictive ML models, with a primary focus on Ad Score and Ad Account Health.

Responsibilities include but are not limited to:

• ML Model Development: Lead the development and refinement of predictive ML models, particularly Ad Score and Ad Account Health.
• Data Analysis: Conduct in-depth data analysis to identify trends, patterns, and insights that inform model development and optimization.
• Feature Engineering: Collaborate with data engineers to create and maintain feature engineering pipelines to support model training.
• Model Evaluation: Implement rigorous evaluation methodologies to assess model performance, making necessary adjustments for continuous improvement.
• Deployment and Integration: Work closely with engineering teams to deploy models and integrate them into our products through APIs.
• Collaboration: Collaborate closely with product managers, full-stack engineers, and TPMs to ensure seamless integration of data science solutions into our products.
• Research and Innovation: Stay up-to-date with the latest developments in the field of data science and machine learning, and explore innovative approaches to problem-solving.

Requirements

1. Master's or Ph.D. in a related field with a strong academic background.
2. Proven experience as a Data Scientist with a track record of developing and deploying predictive ML models.
3. Expertise in machine learning techniques, including but not limited to regression, classification, clustering, and deep learning.
4. Proficiency in data manipulation, feature engineering, and model evaluation.
5. Strong programming skills in languages such as Python and experience with libraries like TensorFlow, PyTorch, or scikit-learn.
6. Excellent communication skills and the ability to collaborate effectively within cross-functional teams.
7. A passion for continuous learning and staying updated with the latest trends and technologies in data science.
8. Strong problem-solving abilities and the capacity to translate complex data into actionable insights.

Required knowledge of:

1. Python
2. SQL
3. Cloud Platforms (GCP, AWS, Azure)
4. Data Warehouses (BigQuery, Snowflake, Redshift)
5. LLMs / AI APIs
6. Git / GitHub

Nice to have:

• Data Transformation (dbt)
• Semantic Layers (Cube, Looker, dbt Metrics)
• TypeScript
• Bayesian modeling experience - ideally Marketing Mix Models (PyMC, Stan, or similar..). Understands priors, MCMC sampling, posterior diagnostics.
• Causal inference / experimentation- geo experiments (matched markets), A/B testing at scale. Familiar with incrementality measurement.
• Marketing/advertising domain- understanding of attribution, media channels (paid social, search, display, video), campaign structures.