Data Scientist

looking for Immediate to 15 days' Notice Period

Data Scientist - AI, Machine Learning & Analytics

Solving for Human Capital business domain problems in the areas of People Analytics & Workforce Intelligence, HR Process Automation & AI Solutions, Employee Experience Analytics, Skills & Learning Intelligence, Payroll Compensation & Benefits Optimization. A few examples in these domains are as follows:

People Analytics & Workforce Intelligence

• Build predictive models for employee attrition, performance, and career progression using advanced ML techniques
• Develop talent acquisition analytics to optimize recruitment processes, predict candidate success, and reduce time-to-hire
• Create workforce planning models to forecast headcount needs, skill gaps, and succession planning scenarios
• Design and implement employee sentiment analysis systems using NLP on survey data, feedback, and communication channels

HR Process Automation & AI Solutions

• Develop intelligent chatbots and virtual assistants for employee queries using LLMs and conversational AI
• Build automated resume screening and candidate matching systems using NLP and semantic search
• Create document processing pipelines for HR documents (contracts, policies, benefits) using Document AI and OCR
• Implement anomaly detection systems for compliance monitoring and risk identification in HR processes

Strategic Analytics & Decision Support

• Design executive dashboards and analytics platforms for C-suite visibility into human capital metrics
• Develop ROI models for HR initiatives including learning programs, wellness benefits, and retention strategies
• Build recommendation systems for personalized employee development paths and learning content
• Create network analysis models to understand organizational dynamics, collaboration patterns, and influence networks

Innovation & Product Development

• Lead POCs for emerging technologies in HR tech (Generative AI for performance reviews, VR/AR for training)
• Develop reusable ML assets and accelerators for the HC Forward platform
• Partner with product teams to integrate data science capabilities into client-facing solutions
• Contribute to thought leadership through whitepapers, client presentations, and industry conferences

Client Engagement & Delivery

• Collaborate with client stakeholders to understand business challenges and translate them into data science solutions
• Present complex analytical findings and recommendations to client leadership teams
• Manage end-to-end delivery of data science projects from requirements gathering to production deployment
• Provide technical expertise during client workshops and strategy sessions
• Travel: Based on client needs

Qualifications

Education

• Bachelor's degree in Mathematics, Statistics, Computer Science, Data Science, or related Engineering disciplines
• Master's degree preferred

Experience

• 5-6 years of hands-on experience in data science, machine learning, and AI
• Proven track record of end-to-end project delivery from POC to production deployment
• Experience leading technical initiatives
• Nice to have team leadership experience (2-4 member teams)

Required Skills

Core Technical Competencies

• Advanced Python Programming: Expertise in Python with production-level code quality, including OOP, API development, and best practices (linting, testing, documentation)
• Machine Learning Mastery: Deep understanding and practical application of:
• Classical ML algorithms (Random Forests, Gradient Boosting, SVM, clustering techniques)
• Deep Learning frameworks (TensorFlow, Keras, PyTorch)
• Time series forecasting and anomaly detection
• Model evaluation, validation, and optimization techniques
• Data Engineering: Experience with data pipelines, ETL processes, and handling large-scale datasets (TB+ scale)
• Cloud Platforms: Hands-on deployment experience with at least one major cloud platform (AWS, Azure, GCP), including:
• Managed ML services (SageMaker, Azure ML, Vertex AI)
• Containerization and orchestration (Docker, Kubernetes)
• Serverless architectures for ML deployment

Any or both of the NLP / ML Engineering skillsets is applicable.

NLP & Text Analytics

• Experience with modern NLP techniques including transformer models (BERT, GPT)
• Text preprocessing, feature extraction, and representation learning
• Practical applications: sentiment analysis, document classification, named entity recognition
• Working knowledge of NLP libraries (NLTK, spaCy, Hugging Face Transformers)

ML Engineering & Production Systems

• MLOps practices: model versioning, monitoring, and automated retraining
• Building scalable ML pipelines and APIs (FastAPI, Flask)
• Experience with distributed computing frameworks (Spark/PySpark)
• Performance optimization and model compression techniques

Desired Skills

Advanced AI/ML Capabilities

• Generative AI & LLMs: Experience with LangChain, RAG architectures, prompt engineering, and fine-tuning large language models
• Computer Vision: Document AI, OCR technologies, image classification using CNNs/YOLO
• Recommendation Systems: Collaborative filtering, content-based filtering, hybrid approaches
• Advanced Analytics: Causal inference, A/B testing, experimental design

Technical Stack

• Big Data Tools: PySpark, Dask, or similar distributed computing frameworks
• Visualization: Creating impactful dashboards using Tableau, Power BI, or Python libraries (Plotly, Dash)
• Version Control & CI/CD: Git workflows, automated testing, and deployment pipelines
• Database Systems: SQL proficiency, experience with NoSQL databases, vector databases

Specialized Domain Experience (Nice to have)

• Experience in HR Analytics, People Analytics, or Workforce Planning
• Knowledge of financial services, healthcare, or retail domains

Domain & Consulting Skills

Client Engagement

• Excellent communication skills to translate complex technical concepts to non-technical stakeholders
• Experience presenting findings and recommendations to product and engineering leaders
• Ability to understand business context and align solutions with strategic objectives

Innovation Mindset

• Demonstrated ability to identify and implement cutting-edge solutions
• Experience in rapid prototyping and POC development
• Contribution to technical communities (publications, open source, speaking engagements)

Business Acumen

• Understanding of business metrics and KPIs
• Ability to identify opportunities for AI/ML applications in business processes