Benefits

Job Description

I. Data Acquisition & Feature Engineering:

Collaborate with stakeholders to identify and prioritize high-value data sources, including internal databases, external APIs, open data sets, web scraping, and online/offline behavioral data.
Design, implement, and maintain robust and scalable data pipelines for ingesting, processing, and transforming data from diverse sources, ensuring data quality, consistency, and security.
Conduct in-depth exploratory data analysis to understand data patterns, identify potential biases, and uncover valuable insights.
Develop advanced feature engineering techniques, including creating new features from raw data, feature selection, and dimensionality reduction, to enhance model performance.
Design and implement a Feature Store to manage and share features across multiple projects and teams, ensuring consistency and reusability.

II. Model Development & Deployment:

Research, select, and implement machine learning and deep learning algorithms and architectures for a variety of business applications, including predictive modeling, classification, clustering, and recommendation systems, targeted marketing, ads exchange.
Build high-performance models using appropriate tools and frameworks (focus on Vertex AI and CDP customer data platform), optimizing for accuracy, scalability, and interpretability.
Develop and deploy models in production environments, using containerization technologies (e.g., Docker), cloud platforms, and APIs.
Continuously monitor model performance, identify and address performance degradation, and implement strategies for model retraining and updates.

Degree in a quantitative field such as Computer Science, Statistics, Mathematics, Operations Research, or a related field.
2+ years of experience in a senior data science or machine learning role, with a proven track record of developing and deploying production-ready models.
Deep understanding of statistical modeling, machine learning algorithms, and data mining techniques, including experience with deep learning, natural language processing, and/or computer vision.
Expert-level proficiency in Python and relevant data science libraries .
Extensive experience with building and deploying data pipelines and machine learning models on cloud platforms (e.g, GCP, CDP, real time) and big data technologies (e.g., Spark, flink).
Strong software engineering skills, including experience with version control (e.g., Git), testing, and CI/CD.
Excellent communication, presentation, and interpersonal skills, with the ability to translate complex technical concepts into clear, actionable insights for diverse audiences.