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.

Experience
3-6 yrs