Data Scientist - Bengaluru, Karnataka, India - Oracle

Oracle

Verified Company

Bengaluru, Karnataka, India

4 weeks ago

Posted by:

Deepika Kaur

beBee Recuiter

Description

About Oracle CrowdTwist
Oracle CrowdTwist provides the most comprehensive omni-channel loyalty & analytics SaaS platform for industry leading brands. We have grown to a respected platform with hundreds of millions of users and billions of interactions. We are looking to write the next chapter in user growth and scale.

We power the loyalty offerings for many of the world's top brands such as Lego, Disney, Marvel Comics, Lenovo, etc.

We're relaxed, experienced, hard driving, changing our industry and looking for smart people like yourself to help tackle tough technical challenges.

About the Opportunity
We are seeking a talented Principal Data Scientist for our growing team based in Bengaluru, India. Our cloud-first SaaS platform leverages Oracle Cloud Infrastructure (OCI) to enable advanced analytics and machine learning. This is a highly MLOps-focused role requiring strong development skills to build and maintain data pipelines and models.

As an IC, the Principal Data Scientist will execute technically on MLOps implementations but also provide technical leadership on MLOps best practices to other associates on the team.

You will develop and maintain critical data pipelines for ETL and feature engineering to support models operationalized in our cloud environment.

Though not a formal people manager role, you will contribute to optimizing MLOps workflows, modeling, and code across the team with your deep hands-on experience.

Working in the cloud, you'll leverage scalable, cloud-based analytics and data infrastructure. Quality, reproducibility, and model operationalization are extremely important. We're seeking a data science technical leader passionate about leveraging the cloud to solve problems and deliver impactful analytics.

Responsibilities:

Collaborate with stakeholders and data teams to identify business challenges amenable to machine learning solutions.
Perform exploratory data analysis to uncover insights and transform business questions into concrete modeling tasks.
Develop, optimize, and enhance machine learning algorithms and neural network architectures for natural language processing, computer vision, forecasting, and other domains.
Build reliable endtoend ML pipelines for data processing, model training, evaluation, and deployment.
Continuously monitor, retrain, and update models to maintain predictive accuracy.
Operationalize and serve models at scale using containers, orchestration, and cloud infrastructure.
Follow software engineering best practices around testing, documentation, modularity, and code quality.

About You:

Have a BA/BS/MS in Computer Science, Statistics, Mathematics, or related quantitative field or equivalent experience
Have 5+ years of professional experience developing and deploying machine learning models and pipelines at scale
Selfstarter who can take initiative, drive consensus, and mentor junior team members
Strong software engineering skills and experience developing in languages like Python, R, and Scala
Experience with statistical and machine learning theory, algorithms, and frameworks like SciKit-Learn, PyTorch, TensorFlow
Ability to learn new data science and ML concepts quickly in a fastpaced environment
Understand model validation, testing, monitoring, and overall ML quality
Have strong SQL skills and experience with largescale databases and data warehouses
Experience operationalizing models into production with tools like Docker, Kubernetes
Experience with big data platforms like Spark, Hadoop, Hive, etc
Experience developing scalable systems processing large amounts of data in a SaaS environment
Knowledge of version control with Git and collaboration tools

Bonus Points:

Experience with distributed computing and largescale data processing tools like Spark, Dask, Ray
Experience developing and optimizing SQL queries on large databases and data warehouses
Knowledge of data governance, metadata management, and data quality best practices