Senior Data Scientist - Data Cloud Oracle
Broomfield, CO

Job Description

Designs, develops and programs methods, processes, and systems to consolidate and analyze unstructured, diverse "big data" sources to generate actionable insights and solutions for client services and product enhancement.

Interacts with product and service teams to identify questions and issues for data analysis and experiments. Develops and codes software programs, algorithms and automated processes to cleanse, integrate and evaluate large datasets from multiple disparate sources. Identifies meaningful insights from large data and metadata sources; interprets and communicates insights and findings from analysis and experiments to product, service, and business managers.

Job duties are varied and complex utilizing independent judgment. May have project lead role. 5 years relevant work experience. BS/BA preferred.

Oracle is an Equal Employment Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability and protected veterans status or any other characteristic protected by law.

The Oracle Data Cloud is an industry leader in connecting online and offline data to execute and measure the effectiveness of marketing initiatives. To enable these insights, the ODC relies on the power of the Oracle Identity Graph to connect thousands of disparate data sources to create comprehensive and accurate anonymized profiles across the numerous ID spaces where marketers are trying to reach consumers. These ID spaces include, but are not limited to, email, mobile phones, tablets, computers, TVs and postal address. Creating these anonymized profiles in a privacy safe and accurate manner is foundational to building targeting audiences at scale as well as detecting a clear signal when measuring the effectiveness of any campaign.

The ODC ID Graph is made possible because of a lot of data and a lot of data science. As a Senior Data Scientist within the Identity Data Science (iDS) Research & Development team, you'll be involved in developing a best-in-class ID Graph that fuels the ODC. In this role, you will be doing a blend of traditional data science work and big data/software engineering. Your responsibility will be to build scalable, cost-conscious, stable, repeatable, and accurate machine learning and ETL pipelines in a cloud environment. This work may span all aspects of the data science and software development lifecycle. To be successful in this role, you must be equally an expert in machine learning and big data/software engineering.

Role

* Develop and maintain production-scale ML systems, including ETL and modeling runtimes.
* Collaborate with other data scientists and engineers to design, research, and implement new data science products.
* Maintain and improve existing analytics solutions.
* Be a source of knowledge and mentorship for data scientists who don't have an engineering background.
* Build tools to help team members and stakeholders interact with and understand our data science products.

Prerequisites

* MS or PhD in Computer Science, Mathematics, Physics, Engineering, Statistics, Econometrics, Operations Research or equivalent industry experience.
* 3 years experience working on large-scale data processing systems in production including data ingestion, normalization, and storage.
* Ability to apply core software engineering principles to practical business and machine learning problems.
* Expert at writing complex SQL queries.
* Understanding of common machine learning models and when to apply them.
* Understanding of the spatial interpretations of modeling features.
* Understanding of machine learning model evaluation techniques; ability to assess, diagnose, and reason about a model's performance.
* Fluency in Scala and Python.
* Experience developing Spark applications at scale, including tuning and debugging.
* Experience with build pipelines (Jenkins) and software build tools (python packaging, sbt, gradle).
* Experience with monitoring production data workflows.
* Experience with cloud computing environments.
* Experience with container-based architecture (Docker or comparable).
* Experience with ElasticSearch and Kibana.
* Ability to develop rapid prototypes and bring those prototypes to production.