Principal Data Scientist

4 weeks ago

bangalore, India Oracle Full time

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, expert, hard driving, changing our industry and looking for inquisitive people like yourself to help solve 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 standard processes 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 very important. We're seeking a data science technical leader passionate about demonstrating 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 end-to-end 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 / or equivalent in Computer Science, Statistics, Mathematics, or related quantitative field or equivalent experience

Have 8+ years of professional experience developing and deploying machine learning models and pipelines at scale

Self-starter 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 fast-paced environment

Understand model validation, testing, monitoring, and overall ML quality

Have strong SQL skills and experience with large-scale 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 large-scale data processing tools like Spark, Dask, Ray Experience applying advanced machine learning techniques like deep learning, reinforcement learning Experience developing and optimizing SQL queries on large databases and data warehouses Knowledge of data governance, metadata management, and data quality best practices

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