Data Scientist

Responsibilities:

• Lead problem definition/refining, solutioning, and implementation of scalable AI/ML solutions in production.
• Lead data collection, data cleaning/preparation, data correctness/completeness/freshness, exploratory data analysis (EDA), feature engineering, etc.
• Extract data from various available data sources without depending on the data engineering team.
• Create POCs to test the hypothesis. Identify explorations that are likely to fail earlier on in the development cycle (EDA/POC phase).
• Help develop a fast culture in the team.
• Create AI/ML models, iteratively improve them, and ensure they meet throughput and latency requirements along with accuracy constraints.
• Lead ensuring hygiene of AI/ML solves post production - periodic retraining, model health monitoring, data health monitoring, alerting/monitoring, etc
• Establish AI/ML model evaluation metrics and link with suitable business metrics.
• Co-own the business metrics and drive improvement in the desired direction.
• Contribute to IP creation through innovation wherever possible.
• Collaborate with industry experts as necessary on niche problems as needed.
• Mentor junior machine learning engineers in the team.
• Keep up with the latest developments/evolutions in AI/ML techniques in chosen problem areas.
• Lead product roadmap and planning along with product managers and other stakeholders, and drive business impact.
• Lead development/enhancement of necessary tooling to improve efficiency of execution, debugging, testing, etc of AI/ML solutions.
• Write detailed and easy-to-understand documentation on deployed models, experiments - ifailed or successful.

Requirements:

• A startup mindset to experiment, fail fast, and learn rapidly.
• Data orientation, excellent data analysis skills.
• Very good at problem-solving.
• 7-8+ years of expertise in developing AI/ML solutions and deploying solutions at a large scale, optimizing further, and driving positive business impact.
• 2+ years in leading a team of data scientists (as a technical mentor) and defining/refining business goals, formulating approaches, etc.
• Good experience working with data technologies (eg SQL, Spark, Hive, Map-Reduce, Scala, etc); streaming technologies (Kafka, Apache Flink / Spark Streaming, or any other similar tools.
• Strong experience and deep understanding of various AI/ML techniques - supervised learning, unsupervised learning, deep learning, and NLP.
• Very good understanding of machine learning frameworks - Keras, Scikit learn, tensorflow, etc.
• Strong experience with Python.
• A good understanding of math is required for AI/ML.
• Experience with AWS or GCP Machine learning platform.