Description
Data Scientist 2/Senior Data Scientist
Location: Chennai, India
Department: Projects & Delivery
Experience: 3-5 years
Skills: LLM, Classical ML, Data Science, Machine Learning
Data Scientist II/ SDS
Experience: 3–5 Years (Relevant Data Science Experience Mandatory)
Location: Chennai (Hybrid)
Company: Akaike Technologies
About Akaike Technologies
Akaike Technologies is an AI-first company focused on delivering large-scale, business-impacting solutions through advanced analytics and intelligent systems. We operate at the intersection of classical machine learning and Generative AI, building scalable, production-grade systems for enterprise clients. Our culture emphasizes ownership, clarity of thought, and measurable impact.
Role Overview
We are looking for a Data Scientist with 3–5 years of relevant experience to contribute to scalable ML systems and Generative AI solutions.
Focus Areas:
70% Classical Machine Learning 30% Generative AI & LLM Applications
The ideal candidate should be capable of:
Handling large-scale data pipelines
Building production-ready ML solutions
Contributing to LLM-driven applications
Translating business problems into quantifiable ML solutions
Key Responsibilities
1. Large-Scale Data Handling & MLOps
- Process and model large datasets using PySpark / Spark SQL
- Build and deploy ML pipelines on Databricks / AWS
- Design scalable workflows using:
- Docker
- AWS (ECR, Lambda, Step Functions)
- Implement real-time or near real-time inference pipelines
- Perform advanced feature engineering aligned to business objectives
2. Classical Machine Learning (Core Focus)
- Build end-to-end ML pipelines:
- Problem framing
- Feature engineering
- Model development
- Evaluation
- Deployment
- Business impact measurement
- Work with:
- Tree-based models (XGBoost, LightGBM)
- GLMs
- Clustering models
- Apply modeling in marketing use cases:
- Targeting
- Budget optimization
- Churn prediction
- Demand forecasting
- Handle constrained-data scenarios:
- Sparse labels
- Single-class learning
- PU learning
- Apply model explainability methods:
- SHAP
- LIME
3. Generative AI & LLM Systems
Build LLM-ready data layers (structured + unstructured)
Work with foundation models such as:
- GPT-4, Mistral, Llama, Claude
Implement:
- Prompt engineering strategies
- LLM evaluation frameworks
- Human-in-the-loop systems
- Confidence grading mechanisms
Contribute to simple agentic workflows:
- ReAct patterns
- Multi-agent collaboration setup
- Fine-tune models where required
Must-Have Technical Skills
- Programming & Data Processing
- Python (NumPy, Pandas, PySpark)
- Experience with distributed computing
- Git / GitHub version control
- Cloud/MLops Technologies: AWS, GCP, Docker
Machine Learning
- Strong understanding of:
- Supervised & Unsupervised learning
- Feature engineering
- Model validation strategies
Experience with:
- Scikit-learn
- TensorFlow / Keras
- PyTorch
- Deep Learning
- ANN basics
- CNN (1D/2D)
- LSTM fundamentals
- Transformer architectures
Soft Skills (Critical for Success)
- Communication
- Clear articulation during client calls
- Ability to simplify complex ML concepts
- Structured thinking with clear next steps
- Presentation
- Whiteboarding ML approaches
- Building business-impact-driven presentations for stakeholders
Problem Solving
- Rapid problem framing
- Pragmatic trade-off decisions
- Delivering within defined timelines
What We Look For:
- Ownership mindset
- Bias toward execution
- Comfort with ambiguity
- Business-first thinking
- Ability to measure and articulate impact
Benefits & Perks:
Competitive ESOP grants
Conference & research publication support
High cross-functional visibility
Opportunity to work on cutting-edge AI systems at scale
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