Machine Learning Engineer

Pecan is an automated AI-based predictive analytics platform. It simplifies and accelerates the process of building and deploying machine learning models in various business use-cases, such as life-time value, Churn, demand forecast and more. Pecan connects to the raw data and completely automates the data preparation, engineering and prepossessing phases, as well as the model training and evaluation lifecycle. It was acknowledged as one of Israel's 50 most promising startups two years in a row

Company Highlights:

• Series C company with over $117M raised to date. Tier-1 investors: Google Ventures (GV), Insight Partners, GGV, Dell Ventures, Mindset and S Capital.
• 80 employees
• HQ in Tel Aviv with growing sales and marketing organization in the US
• Customers across CPG, retail, healthcare, mobile apps, fintech, insurance, and consumer services. Marquee customers include Johnson & Johnson,, and SciPlay..

Responsibilities:

Work on pecans’s core data and machine learning infrastructure, which is at the heart of Pecan's offering. You will create innovative solutions for data ingestion and normalization from multiple data sources, feature engineering and feature selection, as well as actual model training and evaluation. All in large scale and completely automated.

Who You Are:

A problem solver at heart, you have a passion for excellence, you love to learn but know when it’s time to deliver and make ends meet. You aren’t threatened by a complex, dynamic and demanding environment. “There is no I in team”, is a motto you believe in deeply and you are always looking out for your peers. You know how to take ownership and drive projects to completion.

What We're Looking For:

• 3+ years experience as a Machine Learning Engineer.
• 5+ years of experience with Python/Java/Scala.
• Strong understanding of distributed systems, object-oriented programming and design patterns.
• Distributed Compute frameworks such as Spark, Dask, Ray etc
• Hands-on experience designing, training, and deploying machine-learning models
• MLOps
• Hands-on experience with open source ML libraries like: catboost, lightgbm, xgboost, scikit-learn, NumPy, Pandas, Microservices architecture, cloud technologies, Docker/K8s.
• Ability to design and own a feature through all its phases.

Bonus:

• BSc./MSc. In CS or similar - an advantage
• Building data pipelines using Apache Airflow
• Hands on experience with Spark, SparkSQL, Spark streaming and other Spark related projects