Data Science and Machine Learning Senior Associate

Employees in this job function are responsible for predicting and/ or extracting meaningful trends/ patterns/ recommendations from raw data, leveraging data science methodologies including Machine Learning (ML), predictive modeling, math, statistics, advanced analytics, etc.

Key Responsibilities :

1) Understand business requirements and analyze datasets to determine suitable approaches to meet analytic business needs and support data-driven decision-making

2) Design and implement data analysis and ML models, hypotheses, algorithms and experiments to support data driven decision-making

3) Apply various analytics techniques like data mining, predictive modeling, prescriptive modeling, math, statistics, advanced analytics, machine learning models and algorithms, etc.; to analyze data and uncover meaningful patterns, relationships, and trends

4) Design efficient data loading, data augmentation and data analysis techniques to enhance the accuracy and robustness of data science and machine learning models, including scalable models suitable for automation

5) Research, study and stay updated in the domain of data science, machine learning, analytics tools and techniques etc.; and continuously identify avenues for enhancing analysis efficiency, accuracy and robustness

Skills Required :

AI/ML, ALGORITHMS, Deeplearning, Python, LLM, CI-CD, API

Experience Required:

• Senior Associate 8+ years of hands-on experience in ML/Deep Learning, with at least 4+ years in a senior or lead role.
• Hands-on experience building solutions using: OCR pipelines (Tesseract, EasyOCR, PaddleOCR, AWS Textract, Google Vision).
• Document Layout Models (LayoutLMv3, Donut, Pix2Struct, LiLT, LayoutXLM).
• Vision-Language Models (CLIP, BLIP-2, GroundingDINO, LLaVA, Florence-2).
• LLM-based extraction using OpenAI, Gemini, Claude, or open-source models (Llama, Qwen, Mistral). Ability to design end-to-end ML system architecture with: Model orchestration (LLM + OCR + embeddings + prompt pipelines)
• Preprocessing for images/PDF/PPT/Excel Embedding store, vector DB, or structured extraction systems Async processing queue, job orchestration, microservice design GPU/CPU deployment strategy.
• Must be strong in scaling ML systems: Batch processing large files Handling concurrency, throughput, latency Model selection, distillation, quantization (GGUF, ONNX)

Experience Preferred:

• CI/CD for ML (GitHub Actions, Jenkins) Model monitoring (concept drift, latency, cost optimization) Experience with cloud platforms: AWS/GCP/Azure with AI services (SageMaker, Vertex AI, Bedrocknice to have).
• Problem Solving & Solution Ownership Able to identify the right ML approach (fine-tuning, retrieval, prompting, multimodal pipeline).
• Ability to break vague product problems into clear ML tasks. Skilled in PoC building, quick prototyping, and converting them into production systems.
• Capability to estimate feasibility, complexity, cost, and timelines of ML solutions.

Education Required:

Bachelor's Degree, Master's Degree

Education Preferred:

Certification Program

Additional Information:

• Ability to design end-to-end ML system architecture with: Model orchestration (LLM + OCR + embeddings + prompt pipelines)
• Preprocessing for images/PDF/PPT/Excel Embedding store, vector DB, or structured extraction systems Async processing queue, job orchestration, microservice design GPU/CPU deployment strategy
• Must be strong in scaling ML systems: Batch processing large files Handling concurrency, throughput, latency Model selection, distillation, quantization (GGUF, ONNX) CI/CD for ML (GitHub Actions, Jenkins) Model monitoring (concept drift, latency, cost optimization)
• Experience with cloud platforms: AWS/GCP/Azure with AI services (SageMaker, Vertex AI, Bedrocknice to have)
• Problem Solving & Solution Ownership Able to identify the right ML approach (fine-tuning, retrieval, prompting, multimodal pipeline).
• Ability to break vague product problems into clear ML tasks. Skilled in PoC building, quick prototyping, and converting them into production systems. Capability to estimate feasibility, complexity, cost, and timelines of ML solutions.