Data Science - Lead

Job Description

Key Responsibilities:
. Design, implement, and maintain data science pipelines from data ingestion to model deployment.
. Collaborate with data scientists to operationalize ML models and algorithms in production environments.
. Develop robust APIs and services for ML model inference and integration.
. Build and optimize large-scale data processing systems using Spark, Pandas, or similar tools.
. Ensure data quality and pipeline reliability through rigorous testing, validation, and monitoring.
. Work with cloud infrastructure (AWS) for scalable ML deployment.
. Manage model versioning, feature engineering workflows, and experiment tracking.
. Optimize performance of models and pipelines for latency, cost, and throughput.
Required Qualifications:
. Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field.
. 5+ years of experience in a data science, ML engineering, or software engineering role.
. Proficiency in Python (preferred) and SQL knowledge of Java, Scala, or C++ is a plus.
. Experience with data science libraries like Scikit-learn, XGBoost, TensorFlow, or PyTorch.
. Familiarity with ML deployment tools such as ML flow, Sage Maker, or Vertex AI.
. Solid understanding of data structures, algorithms, and software engineering best practices.
. Experience working with databases (SQL, NoSQL) and data lakes (e.g., Delta Lake, Big Query).
Preferred Qualifications:
. Experience with containerization and orchestration (Docker, Kubernetes).
. Experience working in Agile or cross-functional teams.
. Familiarity with streaming data platforms (Kafka, Spark Streaming, Flink).
Soft Skills:
. Strong communication skills to bridge technical and business teams.
. Excellent problem-solving and analytical thinking.
. Self-motivated and capable of working independently or within a team.
. Passion for data and a curiosity-driven mindset.

Mandatory Competencies