Data Scientist

Job Title:

Location:

Experience:

Employment Type:

Compensation:

About the Role:

Data Scientist

Key Responsibilities:

• Develop and deploy

  end-to-end machine learning pipelines

  including data preprocessing, modeling, evaluation, and production deployment.
• Work on cutting-edge

  AI/ML applications

  such as

  LLM-finetuning, NLP, Computer Vision, Hybrid Recommendation Systems

  , and

  RAG/CAG techniques

  .
• Leverage platforms like

  AWS (SageMaker, EC2)

  and

  Databricks

  for scalable model development and deployment.
• Handle data at scale using

  Spark, Python, SQL

  , and integrate with

  NoSQL and Vector Databases (Neo4j, Cassandra)

  .
• Design interactive dashboards and visualizations using

  Tableau

  for actionable insights.
• Collaborate with cross-functional stakeholders to translate business problems into analytical solutions.
• Guide data curation efforts and ensure high-quality training datasets for supervised and unsupervised learning.
• Lead initiatives around

  AutoML, XGBoost, Topic Modeling (LDA/LSA), Doc2Vec

  , and

  Object Detection & Tracking

  .
• Drive agile practices including

  Sprint Planning, Resource Allocation, and Change Management

  .
• Communicate results and recommendations effectively to executive leadership and business teams.
• Mentor junior team members and foster a culture of continuous learning and innovation.

Technical Skills Required:

• Programming:

  Python, SQL, Spark

• Machine Learning & AI:

  NLP, LLMs, Deep Learning, Computer Vision, Hybrid Recommenders

• Techniques:

  RAG, CAG, LLM-Finetuning, Statistical Modeling, AutoML, Doc2Vec

• Data Platforms:

  AWS (SageMaker, EC2), Databricks

• Databases:

  SQL, NoSQL, Neo4j, Cassandra, Vector DBs

• Visualization Tools:

  Tableau

Certifications (Preferred):

• IBM Data Science Specialization
• Deep Learning Nanodegree (Udacity)
• SAFe® DevOps Practitioner
• Certified Agile Scrum Master

Professional Competencies:

• Proven experience in

  team leadership, stakeholder management

  , and

  strategic planning

  .
• Strong

  cross-functional collaboration

  and ability to drive alignment across product, engineering, and analytics teams.
• Excellent problem-solving, communication, and decision-making skills.
• Ability to manage

  conflict resolution, negotiation

  , and performance optimization within teams.