Data Science Engineer

Key Responsibilities

• Model Development & Engineering: Design, train, fine-tune, and deploy

predictive ML models and algorithms to address domain problems (e.g.,

churn forecasting, recommendation systems, portfolio analytics, anomaly

detection).

• NLP Implementation: Develop and maintain NLP pipelines to parse earnings

reports, news articles, financial disclosures, and customer interaction logs.

• MLOps & Productionization: Implement scalable ML pipelines, RESTful

APIs, and MLOps practices for seamless model deployment, continuous

monitoring, and automated retraining.

• Data Analytics & Feature Engineering: Perform exploratory data analysis

(EDA) on structured market data and unstructured platform data to craft

high-impact features.

• Data Pipelines: Collaborate with Data Engineers to build scalable data

pipelines handling streaming tick data, transactional logs, and reference

data.

Required Qualifications & Skills

• Experience: 35 years of experience as a Data Scientist, ML Engineer, or AI

Engineer in a fast-paced environment (Stock Broking, Fintech, or E-

commerce experience is a plus).

• Core Skills: Strong proficiency in Python, SQL, and Machine Learning

libraries (Scikit-Learn, PyTorch, TensorFlow, XGBoost).

• NLP Expertise: Hands-on experience with modern NLP frameworks

(Hugging Face, Transformers, spaCy, NLTK) and LLM application

frameworks (LangChain, LlamaIndex).

• MLOps & Cloud: Experience with Docker, Kubernetes, CI/CD for ML, cloud

environments (AWS/GCP/Azure), and feature stores.

• Data Analytics: Proficiency in handling high-volume datasets using SQL,

Pandas, PySpark, and visualization libraries.

• Education: Bachelor’s or Master’s degree in Computer Science, Data

Science, Engineering, Statistics,