Senior Data Scientist - East Coast

Senior Data ScientistLocation: East Coast, USA

About Oracle CrowdTwist:

Oracle CrowdTwist, based in NYC, offers a leading omni-channel loyalty and analytics SaaS platform trusted by industry giants. Our platform, powering loyalty solutions for top brands like Lego, Chipotle, Marvel Comics, and Lenovo, is renowned for its scale and innovation. We’re a driven, dynamic team committed to pushing boundaries and solving complex technical challenges. Join us as we write the next chapter in user growth and scale.

About the Opportunity:

We are seeking a talented Senior Data Scientist to join our expanding data team. Leveraging Oracle Cloud Infrastructure (OCI), our cloud-first SaaS platform enables advanced analytics and machine learning. This role will focus on developing and deploying machine learning models to enhance our products. As a senior team member, you will provide technical leadership and mentorship, ensuring best practices and high-quality outcomes.

Responsibilities:

• Collaborate with Stakeholders: Engage with stakeholders to understand business challenges and identify opportunities for machine learning solutions.
• Exploratory Data Analysis: Conduct exploratory data analysis to extract insights and define modeling objectives.
• Develop and Optimize Models: Design, develop, and refine machine learning algorithms for various applications including regression and clustering.
• Build ML Pipelines: Create and maintain end-to-end ML pipelines for data processing, model training, evaluation, and deployment.
• Model Monitoring: Continuously monitor and update models to maintain predictive accuracy.
• Mentorship: Provide guidance and support to junior data scientists, promoting best practices and high standards.
• Integration: Work closely with product teams to integrate machine learning models into products and drive meaningful outcomes.
• Best Practices: Adhere to software engineering best practices, including rigorous testing, documentation, and code quality.

About You:

• Experience: Must have 6+ years in developing and deploying machine learning models at scale, with a proven track record in a product development environment.
• Education: BA/BS/MS in Computer Science, Statistics, Mathematics, or a related quantitative field, or equivalent experience.
• Technical Skills: Proficiency in Python, SQL, R, or Scala. Deep understanding of machine learning algorithms and frameworks (e.g., SciKit-Learn, PyTorch, TensorFlow).
• Model Management: Experience with model validation, testing, monitoring, and ensuring ML quality.
• Big Data: Familiarity with large-scale databases and data warehouses, and experience with big data platforms like Spark, Hadoop, Hive.
• Deployment Tools: Knowledge of Docker, Kubernetes, and cloud-native tools for model deployment.
• Adaptability: Ability to thrive in a fast-paced environment and adapt to new technologies quickly.
• Communication: Strong communication and collaboration skills, with a capacity to mentor and guide junior team members.

Bonus Points:

• Distributed Computing: Experience with distributed computing tools like Spark, Dask, or Ray.
• Advanced Techniques: Knowledge of advanced machine learning techniques such as deep learning and reinforcement learning.
• SQL Optimization: Experience optimizing SQL queries on large databases and data warehouses.
• Data Governance: Familiarity with data governance, metadata management, and data quality best practices.

 

Career Level - IC3

Disclaimer:

Certain US customer or client-facing roles may be required to comply with applicable requirements, such as immunization and occupational health mandates.

Range and benefit information provided in this posting are specific to the stated locations only

US: Hiring Range: from $86,700 to $199,500 per annum. May be eligible for bonus and equity.

Oracle maintains broad salary ranges for its roles in order to account for variations in knowledge, skills, experience, market conditions and locations, as well as reflect Oracle’s differing products, industries and lines of business.
Candidates are typically placed into the range based on the preceding factors as well as internal peer equity.

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