Data Scientist

Role Overview: As a Data Scientist/Machine Learning/AI Engineer with 2+ years of hands-on experience, your role will involve partnering with Product to identify high-leverage ML opportunities tied to business metrics. You will be responsible for wrangling large structured and unstructured datasets, building reliable features and data contracts, and developing models to enhance customer experiences, boost revenue, and detect fraud/risk in real time. Collaboration with Engineering to productionize models via APIs/CI/CD/Docker on AWS will also be a key aspect of your role. Additionally, you will design and conduct A/B tests, build monitoring for model/data drift and business KPIs, and work on various use cases such as recommendation systems, image data, fraud/risk detection, and more. Key Responsibilities: - Partner with Product to identify high-leverage ML opportunities - Wrangle large structured and unstructured datasets to build reliable features and data contracts - Build and ship models to enhance customer experiences, boost revenue, and detect fraud/risk in real time - Collaborate with Engineering to productionize models via APIs/CI/CD/Docker on AWS - Design and run A/B tests, build monitoring for model/data drift and business KPIs - Work on various use cases such as recommendation systems, image data, fraud/risk detection, price modeling, and propensity models Qualifications Required: - 2+ years of experience in Data Science/Machine Learning in consumer internet/B2C products - Proven success in at least two of the following areas: recommender systems, fraud/risk detection, pricing models, propensity models - Strong programming skills in Python and SQL, with experience in git, Docker, CI/CD - Experience with AWS or GCP, familiarity with warehouses/dashboards (Redshift/BigQuery, Looker/Tableau) - Broad knowledge in ML areas such as recommender systems, NLP, anomaly detection - Excellent communication skills with the ability to tell clear stories with data and drive decisions Company Details: The company prefers candidates from product companies. Role Overview: As a Data Scientist/Machine Learning/AI Engineer with 2+ years of hands-on experience, your role will involve partnering with Product to identify high-leverage ML opportunities tied to business metrics. You will be responsible for wrangling large structured and unstructured datasets, building reliable features and data contracts, and developing models to enhance customer experiences, boost revenue, and detect fraud/risk in real time. Collaboration with Engineering to productionize models via APIs/CI/CD/Docker on AWS will also be a key aspect of your role. Additionally, you will design and conduct A/B tests, build monitoring for model/data drift and business KPIs, and work on various use cases such as recommendation systems, image data, fraud/risk detection, and more. Key Responsibilities: - Partner with Product to identify high-leverage ML opportunities - Wrangle large structured and unstructured datasets to build reliable features and data contracts - Build and ship models to enhance customer experiences, boost revenue, and detect fraud/risk in real time - Collaborate with Engineering to productionize models via APIs/CI/CD/Docker on AWS - Design and run A/B tests, build monitoring for model/data drift and business KPIs - Work on various use cases such as recommendation systems, image data, fraud/risk detection, price modeling, and propensity models Qualifications Required: - 2+ years of experience in Data Science/Machine Learning in consumer internet/B2C products - Proven success in at least two of the following areas: recommender systems, fraud/risk detection, pricing models, propensity models - Strong programming skills in Python and SQL, with experience in git, Docker, CI/CD - Experience with AWS or GCP, familiarity with warehouses/dashboards (Redshift/BigQuery, Looker/Tableau) - Broad knowledge in ML areas such as recommender systems, NLP, anomaly detection - Excellent communication skills with the ability to tell clear stories with data and drive decisions Company Details: The company prefers candidates from product companies.