Senior Data Scientist

IndiaRemoteFull-timesenior

Data ScientistData

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Quick Summary

Key Responsibilities

Design, build, and deploy production-grade machine learning systems, taking solutions from initial prototypes through to production environments. Improve the reliability, accuracy, scalability,

Requirements Summary

The ideal candidate is an experienced machine learning professional with strong research capabilities, practical software engineering expertise,

Technical Tools

Data ScientistData

The Senior Data Scientist will play a key role in advancing AI and machine learning capabilities within an innovative enterprise automation environment. This position combines applied research, hands-on software engineering, and the development of production-ready machine learning systems. You will design and deploy advanced AI solutions, transforming complex operational data into actionable intelligence for IT operations and AIOps applications. Working closely with engineering leaders, product managers, and cross-functional teams, you will influence technical decisions and drive innovation from experimentation to production. You will also contribute to improving the reliability, scalability, and performance of intelligent services. This is an opportunity to work on cutting-edge AI technologies while helping shape the next generation of enterprise automation solutions.

In this role, you will lead the development and deployment of advanced machine learning solutions, combining research-driven innovation with strong engineering practices. Your responsibilities will include:

Design, build, and deploy production-grade machine learning systems, taking solutions from initial prototypes through to production environments.
Improve the reliability, accuracy, scalability, and maintainability of existing machine learning services.
Research and implement innovative AI and machine learning approaches, staying up to date with emerging technologies, methodologies, and scientific developments.
Develop machine learning solutions for AIOps and IT operations using large-scale operational datasets.
Translate research findings and scientific publications into practical, production-ready implementations.
Design and optimize machine learning models, including model selection, feature engineering, and performance optimization strategies.
Apply statistical modeling, time-series analysis, anomaly detection, and other advanced machine learning techniques to solve complex operational challenges.
Collaborate with product managers, data scientists, engineering teams, and other stakeholders to align AI/ML solutions with business and product objectives.
Work closely with AI Systems Technical Leads to deliver optimized AI systems and services into production.
Communicate technical findings, insights, and recommendations clearly to both technical and non-technical stakeholders.
Document research, development processes, model performance, and implementation decisions.
Establish and maintain best practices for ML experimentation, model versioning, reproducibility, testing, and deployment.
Take ownership of production system performance, proactively identifying technical issues and implementing effective solutions.
Guide engineering decisions and mentor other team members, promoting high standards in system design, code quality, and AI-assisted development.
Identify technical debt, scalability challenges, and opportunities for continuous improvement across machine learning systems.

Requirements

~2 min read

The ideal candidate is an experienced machine learning professional with strong research capabilities, practical software engineering expertise, and a passion for bringing advanced AI solutions into production. You should bring:

A Bachelor's degree in Computer Science, Engineering, Mathematics, or a related discipline. An advanced degree in AI, Machine Learning, or a related field is preferred.
At least 4 years of experience in machine learning engineering, with demonstrated advanced development or research capabilities through innovative projects, publications, or academic theses.
Strong understanding of statistical modeling, machine learning fundamentals, and time-series analysis. Knowledge of deep learning is an advantage.
Advanced Python programming skills and experience with machine learning frameworks and libraries such as PyTorch, TensorFlow, scikit-learn, and Hugging Face.
Hands-on experience with unsupervised learning and clustering techniques, including applications involving unstructured text data.
Experience with anomaly detection and time-series forecasting using approaches such as Prophet, VAR, and deep learning models.
Working knowledge of causal inference and root-cause analysis methodologies.
Ability to interpret scientific research papers and translate theoretical approaches into practical implementations.
Strong software engineering fundamentals, including testing, version control, code reviews, and maintainable code development.
Experience building and operating production machine learning services involving APIs, streaming data, SQL, containers, and cloud environments.
Essential experience in Natural Language Processing (NLP).
Familiarity with machine learning experiment tracking, model versioning, and reproducible research practices.
Research publications or open-source contributions in machine learning are preferred.
Previous experience in AIOps, observability, or IT Service Management (ITSM) is an advantage.
Excellent collaboration and communication skills, with the ability to work effectively across product, data science, and engineering teams.
Strong analytical thinking and problem-solving abilities, particularly when diagnosing complex production issues.
A strong sense of ownership and accountability, with a focus on system reliability, performance, and operational excellence.
Technical leadership and mentoring capabilities, with the ability to guide colleagues and promote engineering best practices.
A continuous improvement mindset and proactive approach to identifying technical challenges and innovative solutions.