Data Scientist - Bengaluru, India - Unilever

Unilever

Verified Company

Bengaluru, India

3 weeks ago

Posted by:

Deepika Kaur

beBee Recuiter

Description

Job Title:
Data Scientist - Hyper Automation

Location:
Bangalore

ABOUT UNILEVER:
Be part of the world's most successful, purpose-led business.

Work with brands that are well-loved around the world, that improve the lives of our consumers and the communities around us.

We promote innovation, big and small, to make our business win and grow; and we believe in business as a force for good.

Unleash your curiosity, challenge ideas and disrupt processes; use your energy to make this happen. Our brilliant business leaders and colleagues provide mentorship and inspiration, so you can be at your best.

Every day, nine out of ten Indian households use our products to feel good, look good and get more out of life - giving us a unique opportunity to build a brighter future.

Every individual here can bring their purpose to life through their work. Join us and you'll be surrounded by inspiring leaders and supportive peers. Among them, you'll channel your purpose, bring fresh ideas to the table, and simply be you.

As you work to make a real impact on the business and the world, we'll work to help you become a better you.

Background

The Hyperautomation CoE in UniOps drives Automation agenda across Unilever business units, Experience teams and IT Platforms. Opportunity evaluation shows that we can scale 10X from current scope, what we have delivered in last 4 years.

Simplifying operational tasks frees our time and resources to unlock growth for Unilever and more value for our customers and our partners.

Main purpose of job:
This Role primarily is to design solutions involving AI / ML algorithms to solve business problems through automation.

Key accountabilities:

Design and evaluate machine learning models to drive business value
Work closely with ML engineering and platform team to help define the vision of Machine Learning training and inference platforms running on the cloud
Break down larger ML initiatives into smaller problems that enables data science to deliver incremental business value and guide the team to execute on them
Mentor and grow other data scientists and engineers in the team on both data science ML
Incorporate uptodate ML technology and DS approach as best practice for the team
Help in continuing to build out and expand the Data Science and ML Engineering teams
Work effectively in a dynamic, changing environment while focusing on key goals and objectives
Experience with Hadoop, Spark, or other distributed computing systems for largescale training & prediction with ML models
Endtoend system design:

data analysis, feature engineering, technique selection & implementation, debugging, and maintenance in production.- Experience implementing machine learning algorithms or research papers from scratch- Experience with TensorFlow/PyTorch and deep learning models is a plus

Experience and qualifications required:

Advanced degree (Masters) in Data Science, Statistics, Applied Math, Computer Science, Engineering or other equivalent quantitative disciplines
A/B testing & analysis of ML models, and optimizing models for accuracy
Overall 8+ years and 5+ years of industry experience in the field of Data Science and Machine Learning
Strong proficiency in Python and SQL; experience with some of the following languages, tools, and frameworks: R, Spark, Scala, scikitlearn, Tensorflow, PyTorch, etc.
Strong knowledge of underlying mathematical foundations of statistics and machine learning. Solid understanding of probability, statistics, machine learning, data science
Prior experience in deploying machine learning solutions in largescale production environments
Experience on one or more of the following areas: natural language processing, recommender systems, and deep learning
Experience collaborating with crossfunctional teams and stakeholders to evaluate new Machine Learning opportunities
Problem solver who can formulate solutions and can communicate their findings to crossfunctional stakeholders

Key interfaces- Counterparts in various IT platforms like ERP, Integration, Customer Development, Supply Chain etc- Business stakeholders across globe- Process Excellence- Suppliers