Data Scientist (AI, ML & Analytics)

Overview

Data Scientist

Key Responsibilities

People Analytics & Workforce Intelligence

• Build predictive models for attrition, performance, talent acquisition, and career progression.
  
• Develop workforce planning models to forecast headcount, skill gaps, and succession needs.
  
• Conduct employee sentiment analysis using NLP on surveys, feedback, and communication data.
  

HR Process Automation & AI Solutions

• Design and develop conversational AI solutions including chatbots and virtual assistants.
  
• Automate resume screening and candidate-to-role matching using NLP and semantic search.
  
• Implement document processing pipelines using OCR and Document AI.
  
• Build anomaly-detection systems for compliance and risk identification.
  

Strategic Analytics & Decision Support

• Build executive dashboards offering insights into workforce metrics and HR programs.
  
• Conduct ROI analysis for HR initiatives, including L&D and retention strategies.
  
• Develop recommendation engines for personalized learning and employee development.
  
• Apply organization network analysis to evaluate collaboration patterns and influence.
  

Innovation & Product Development

• Build prototypes and POCs leveraging emerging technologies (GenAI, VR/AR).
  
• Develop reusable ML accelerators and assets for platform integration.
  
• Partner with product teams to deliver scalable data science capabilities.
  
• Contribute to thought leadership via whitepapers, presentations, and conferences.
  

Client Engagement & Delivery

• Liaise with client stakeholders to gather requirements and define problem statements.
  
• Present analytical solutions and findings to business and technical leadership.
  
• Lead end-to-end delivery from design to production deployment.
  
• Support client workshops and strategy discussions.
  
• Travel as required based on client/project needs.
  

Required Skills

Core Technical Competencies

• Expert Python programming; OOP, API development, testing, and documentation
  
• Strong ML background: Random Forest, SVM, Gradient Boosting, clustering
  
• Experience with deep learning frameworks (TensorFlow, Keras, PyTorch)
  
• Time-series analysis, anomaly detection, and model optimization
  
• Data engineering: ETL, pipeline development, large-scale dataset handling (TB+)
  

Cloud & MLOps

• Hands-on with cloud platforms: AWS, Azure, or GCP
  
• Experience with ML services (SageMaker, AzureML, Vertex AI)
  
• Knowledge of Docker/Kubernetes and serverless architectures
  
• MLOps practices: model versioning, monitoring, automated retraining
  
• ML pipeline and API development (FastAPI, Flask)
  

NLP & Text Analytics (preferred)

• Transformer models (BERT, GPT)
  
• Experience with NER, sentiment analysis, document classification
  
• Proficiency with NLP libraries (NLTK, spaCy, HuggingFace)
  

Big Data & Analytics

• Proficient in Spark/PySpark or similar distributed computing
  
• Advanced SQL; NoSQL and vector database experience
  
• Data visualization tools: Tableau, Power BI, or Python (Plotly, Dash)
  

Soft Skills

• Strong communication and storytelling abilities
  
• Client-facing experience with executive presentations
  
• Strong business acumen and problem-solving mindset
  
• Ability to innovate and perform rapid prototyping

Note: NP_ Immediate to 30days only

Interested candidates, Kindly share updated Resume, Pan Card and Photo @ rehanabegum.naddimulla@kiya.ai