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 setups
• 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