Principal Data Scientist

1 week ago

Bengaluru, Karnataka, India Oracle Full time
About Oracle CrowdTwistOracle 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 OpportunityWe 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 experienceHave 8+ years of professional experience developing and deploying machine learning models and pipelines at scaleSelf-starter who can take initiative, drive consensus, and mentor junior team membersStrong software engineering skills and experience developing in languages like Python, R, and ScalaExperience with statistical and machine learning theory, algorithms, and frameworks like SciKit-Learn, PyTorch, TensorFlowAbility to learn new data science and ML concepts quickly in a fast-paced environmentUnderstand model validation, testing, monitoring, and overall ML qualityHave strong SQL skills and experience with large-scale databases and data warehousesExperience operationalizing models into production with tools like Docker, KubernetesExperience with big data platforms like Spark, Hadoop, Hive, etcExperience developing scalable systems processing large amounts of data in a SaaS environmentKnowledge of version control with Git and collaboration toolsBonus Points:
Experience with distributed computing and large-scale data processing tools like Spark, Dask, RayExperience applying advanced machine learning techniques like deep learning, reinforcement learningExperience developing and optimizing SQL queries on large databases and data warehousesKnowledge of data governance, metadata management, and data quality best practices

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