Data Scientist - Azure/Python/Machine Learning

Aarizon Services
Bangalore
6 - 10 Years

Posted on: 05/06/2025

Job Description

Overview :


The ideal candidate should have a strong background in working different cloud environments (GCP, Azure or AWS), CI\CD WoW, Migration process, MLOps, LLMOps and Data Engineering.


Responsibilities :


- Collaborate closely with clients and stakeholders in the CG&S domain to grasp business requirements and user needs, translating

them into effective machine learning solutions.

- Provide expert consultation and guidance to internal stakeholders on best practices in machine learning, utilizing Azure Services, Data Science methodologies, Python programming, DevOps/GitHub integration, and data assessment techniques.

- Lead the development and implementation of data pipelines tailored to various sources, including Nielsen, Kantar, IRI, EPOS, public data, E-Comm, and Brand Health data, ensuring cloud-agnostic compatibility across AWS, Azure, and GCP.

- Utilize advanced modelling techniques and feature synthesis methodologies to extract actionable insights from diverse datasets.

- Conduct comprehensive data quality checks and implement measures for continuous improvement.

- Drive the creation of analytical models, leveraging techniques such as LLM (Large Language Models) and Generative AI, to address

complex business challenges.

- Manage end-to-end project lifecycles, from initial scoping to deployment and maintenance, ensuring adherence to timelines and budget constraints.

- Stay updated on emerging trends and advancements in machine learning, contributing insights and recommendations to the team and wider organization.

- Collaborate effectively with data scientists, analysts, and engineers to seamlessly integrate machine learning models into data-driven applications and products.

- Contribute to thought leadership initiatives by sharing expertise through mediums such as blog posts, whitepapers, and presentations, fostering knowledge sharing within the organisations


Qualifications :


Who we are looking for?

- Minimum of 5 to 7 years of experience in machine learning engineering, data science, or related roles, demonstrating a robust track record of successful projects and achievements.

- The candidate should be experienced in dealing with CG&S data (Nielsen, EPOS, IRI, Kantar, Euromonitor, Manufacturer or retailer P&L - financials, SAP, Sales & operations, Demographics, Trade promotion planner etc.) & functions (such as Customer Experience, Marketing and Promotions, Store Planning, Supply Chain and Logistics, and trade promotions, salesforce etc.) driven through AI.

- Proficiency in utilizing Azure Services, Python programming, and DevOps/GitHub for developing and deploying machine learning solutions.

- Strong expertise in data assessment, modelling, data feature synthesis, and conducting data quality checks to ensure accuracy and reliability.

- Proven experience in analytical model creation, leveraging techniques such as LLM (Large Language Models) and Generative AI to derive actionable insights.

- Demonstrated capability in project management, overseeing end-to-end project lifecycles from inception to deployment.

- Substantial knowledge and experience in cloud agnostic environments, including AWS, Azure, and GCP, with a focus on building

robust data pipelines for diverse datasets, including Nielsen, Kantar, IRI, EPOS, public data, E-Comm, and Brand Health data.

- Excellent communication and collaboration skills, with the ability to effectively convey complex concepts and findings to diverse stakeholders.

- Experience working in agile environments and collaborating closely with cross-functional teams to deliver impactful solutions.

- A background in statistics, mathematics, or related quantitative fields is preferred, with additional qualifications such as an MBA from a premier business school considered a plus.


Note : Candidates must have their own Laptop. This is a contract job role and you will be working as a consultant.


The job is for:

Women candidates preferred
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