Data Science Engineer

Spectrum; Mumbai; On-site; INR 1,800,000 - 2,500,000; Full time

Job summary

Spectrum is seeking a seasoned Data Scientist/ML Engineer to design, train, and deploy predictive ML models and NLP pipelines for diverse domains in a fast-paced environment.

You will build scalable MLOps-enabled pipelines, RESTful APIs, and automated retraining while performing data analytics and feature engineering on large-scale datasets. Collaboration with data engineers is essential for streaming data and reference data processing.

Qualifications

• 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).
• Strong proficiency in Python, SQL, and ML libraries (Scikit-Learn, PyTorch, TensorFlow, XGBoost).
• NLP expertise with modern frameworks (Hugging Face, Transformers, spaCy, NLTK) and LLM application frameworks (LangChain, LlamaIndex).
• MLOps & cloud experience with Docker, Kubernetes, CI/CD for ML, and cloud environments (AWS/GCP/Azure).
• Data analytics using SQL, Pandas, PySpark and visualization libraries.

Responsibilities

• Model Development & Engineering: Design, train, fine-tune, and deploy predictive ML models.
• NLP Implementation: Develop and maintain NLP pipelines to parse earnings reports, news articles, and logs.
• MLOps & Productionization: Build scalable ML pipelines, RESTful APIs, and automated retraining.
• Data Analytics & Feature Engineering: Conduct EDA on structured and unstructured data to craft features.
• Data Pipelines: Collaborate with Data Engineers to build scalable pipelines for streaming data and logs.

Skills

Python

SQL

ML libraries

NLP frameworks

MLOps

Data analytics

Education

Bachelor's or Master's in CS/DS/Engineering

Tools

Docker

Kubernetes

Cloud (AWS/GCP/Azure)

LangChain

LlamaIndex

PySpark

Job description

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,