Associate Lead Data Scientist

Location Name:

Job Purpose

Duties And Responsibilities

•  Contribute to the development and optimization of enterprise-wide search systems and models.
•  Design and implement algorithms to improve indexing, query relevance, and search accuracy.
•  Support taxonomy, ontology, and metadata model creation for better search outcomes.
•  Collaborate with business units (Loans, Insurance, Investments) to build AI-enabled search features.
•  Conduct analysis of user behavior and system metrics to refine search performance.
•  Work with engineers, product managers, and designers to deliver integrated search solutions.
•  Develop production-grade ML systems for ranking, personalization, and recommendations.
•  Participate in proof-of-concept initiatives with internal and external partners.
•  Follow best practices in software engineering including CI/CD, testing, and monitoring.
•  Keep abreast of emerging developments in AI/ML to apply them in practical solutions.
  

Key Decisions / Dimensions

•  Design and coding approaches for assigned modules.
•  Choice of algorithms, tools, and frameworks for implementation.
•  Recommendations on improving performance, accuracy, or customer experience.
•  Task prioritization within assigned projects.
  

Major Challenges

•  Designing scalable models that can support high-volume search traffic.
•  Balancing performance, accuracy, and latency in production search systems.
•  Keeping up with advancements in AI/ML while aligning with business priorities.
•  Ensuring reliable and seamless customer search experience.
  

Educational Qualifications

Required Qualifications and Experience

•  BTech/MTech/BE/MSc in Computer Science, Mathematics, Statistics, or related fields.
  

Work Experience

•  5–8 years in Search, Information Retrieval, NLP, and Machine Learning.
•  Hands-on experience in deploying ML/AI-based systems at scale.
•  Experience mentoring junior engineers (preferred).
  

Mandatory Skills

•  Strong knowledge of search technologies (indexing, faceted search, NLP-based search).
•  Programming skills in Python, Java, or Scala.
•  Familiarity with ML/DL frameworks: TensorFlow, PyTorch, scikit-learn, Keras.
•  Experience with SQL/NoSQL databases (Cosmos DB, MongoDB, Cassandra).
•  Exposure to CI/CD pipelines, Git, Jenkins, testing frameworks.
•  Good understanding of data structures, algorithms, OOP concepts, and Linux.
  

Preferred Skills (Nice To Have)

•  Experience with Elasticsearch/Solr, Lucene-based engines.
•  Knowledge of distributed systems and container platforms (Docker/Kubernetes).
•  Understanding of cloud-based ML deployments (Azure/AWS/GCP).
•  Exposure to personalization/recommendation systems.
•  Research exposure or certifications in AI/ML.