Data Scientist (India - remote)

Anson McCade Pty; India; On-site; INR 1,500,000 - 2,500,000; Full time

Job summary

Anson McCade Pty is seeking an experienced Data Scientist to join a team solving complex business problems through advanced analytics and large-scale data science. You will work across the full lifecycle, from problem understanding to deploying ML models at scale.

You’ll collaborate with consulting teams and clients, translating challenges into practical data science solutions and contributing to reusable analytical frameworks across industries. Travel may be required.

Qualifications

• A degree in a quantitative discipline such as Computer Science, Engineering, Econometrics, Statistics, Business Analytics, Informatics or a related field.
• 2–6 years of professional experience in data science, analytics or a closely related discipline.
• Strong interpersonal and communication skills to explain complex ML concepts to stakeholders.
• Solid understanding of supervised, unsupervised and reinforcement learning techniques.
• Experience with ML frameworks and tools such as scikit-learn and PyTorch.
• Practical experience with MLOps, deployment and management of production workflows.
• Experience with model explainability and interpretability techniques.
• Experience with Docker, containers and Git.
• Experience with large-scale distributed computing and cloud platforms (AWS, Azure, GCP).
• Experience building robust, reusable, production-ready analytical solutions.

Responsibilities

• Work closely with consulting teams and clients to translate business problems into data science projects.
• Define, prioritize and execute analytical solutions based on business needs.
• Develop and implement tools for data acquisition, transformation and management of large datasets.
• Prototype and test ML solutions across diverse data types and scales.
• Apply a range of ML techniques to real-world problems.
• Develop scalable models, frameworks and reusable components for repeatable problems.
• Contribute to MLOps, supporting deployment and production workflows.
• Improve model explainability and communicate results to stakeholders.
• Collaborate with engineering to deploy robust solutions in production.
• Share knowledge and best practices within the data science community.
• Stay updated on emerging DS/ML/AI technologies and opportunities.

Skills

Data science

Machine learning

Communication

Analytical thinking

Education

Degree in Computer Science/Econometrics/Statistics/Engineering/Analytics

Tools

Docker

Git

scikit-learn

PyTorch

AWS

Azure

GCP

Job description

Confidential opportunity with a global strategy firm

Our client is looking for an experienced Data Scientist to join a growing team focused on solving complex business problems through advanced analytics, machine learning and large-scale data science.

This is a hands-on role working closely with consulting teams and clients to translate business challenges into practical data science solutions. You’ll work across the full data science lifecycle, from understanding problems and working with complex datasets through to developing, testing and deploying machine learning models at scale.

The role offers exposure to a diverse range of industries, data environments and use cases, with opportunities to work alongside experienced data scientists, engineers and consultants on high-impact projects.

What You’ll Do

• Work closely with consulting teams and clients to understand complex business problems and translate them into data science projects.


• Help define, prioritise and execute analytical and data-driven solutions based on business needs.


• Develop and implement tools and processes for the acquisition, extraction, transformation, management and manipulation of large and complex datasets.


• Develop, prototype and test machine learning solutions across a wide variety of data types and scales — from smaller datasets through to billions of data points.


• Apply a broad range of machine learning techniques to solve real-world business problems.


• Develop scalable and reusable models, frameworks and components that can address repeatable problems across industries and business functions.


• Contribute to MLOps practices, supporting the development, deployment and ongoing management of production machine learning workflows.


• Apply model explainability and interpretability techniques to help ensure models can be understood and effectively communicated to stakeholders.


• Work collaboratively with engineering and data science teams to deploy robust, scalable solutions in production environments.


• Share technical knowledge and best practices with colleagues across the wider data science community.


• Contribute to the development and adoption of reusable analytical approaches and frameworks.


• Stay current with emerging data science, machine learning and AI technologies and identify opportunities to apply them to new business problems.


• Engage with the wider open-source and data science community where appropriate.


• Travel may be required depending on project needs.

What We’re Looking For

We’re looking for a technically strong Data Scientist with solid foundations in machine learning and experience applying data science to real-world problems.

You’ll ideally bring:

• A degree in a quantitative discipline such as Computer Science, Engineering, Econometrics, Statistics, Business Analytics, Informatics or a related field.


• 2–6 years of professional experience in data science, analytics or a closely related discipline.


• Strong interpersonal and communication skills, including the ability to explain complex mathematical and machine learning concepts to both technical and non-technical stakeholders.


• Solid understanding of supervised, unsupervised and reinforcement learning techniques, including classification, clustering, dimensionality reduction, regression and deep learning.


• Experience with machine learning frameworks and tools such as scikit-learn, PyTorch or similar.


• Practical experience with MLOps, including scalable development, deployment and management of complex data science workflows.


• Experience with model explainability and interpretability techniques.


• Experience working with Docker, containers and Git.


• Experience with large-scale distributed computing and cloud platforms such as AWS, Azure and/or GCP.


• Strong understanding of data preparation, transformation and manipulation across large and complex datasets.


• Experience developing robust, reusable and production-ready analytical solutions.

Experience with modern AI/LLM frameworks and platforms, including OpenAI-based technologies, would be an advantage.