Senior Data Scientist

Urgently hiring Senior Data Scientist!

Is your CV ready If so, and you are confident this is the role for you, make sure to apply asap.

This Jobot Job is hosted by : Kendall Kaing

Are you a fit? Easy Apply now by clicking the "Apply Now" button and sending us your resume.

Salary : $80,000 - $130,000 per year

A bit about us :

Are you interested in working for the world's largest cannabis market with a footprint that covers the entire breadth of the state of California? Are you someone who wants to be part of the growth of a fast-growing industry? At client company, our mission is clear : to provide the ultimate one stop shop cannabis experience by offering exceptional customer service and diversified products. We strive to build long-term customer loyalty. We’re building a consumer-centric organization that is focused on sharing the transformational potential of cannabis with the world.

Our product line is one of the best-selling cannabis brands in the market today and has claimed the title of the best-selling vape brand across all BDSA-tracked markets and best-selling brand overall in the California market! We are rooted in California and have expanded our operations across the United States, with even more growth on the horizon! Additionally, we’re building distribution networks to bring our products to over 60 countries worldwide.

We recognize that our employees are at the center of our success, and we take pride in a corporate culture that emphasizes our core values : Influence, Inspire, Innovate, Win, & Grow!

Our employees come from a wide range of retail backgrounds, each bringing their own unique skills and talents to the table as we work together to continue our incredible growth. If you are interested in partaking in the journey of building a nationally recognized and leading brand, we want to hear from you!

Why join us?

Benefits & Compensation :

Additional details about compensation and benefits eligibility for this role will be provided during the hiring process. All employees are provided competitive compensation, paid training, and employee discounts on our products and services. We offer a range of benefits packages based on employee eligibility, including :

Paid Vacation Time, Paid Sick Leave, Paid Holidays, Parental Leave.

Health, Dental, and Vision Insurance.

Employee Assistance Program.

401k with generous employer match.

Life Insurance.

Job Details

Why this role

You’ll architect, build, and run the data backbone that powers analytics, ML, and operational reporting.

What you’ll do

• Own the lakehouse. Design and evolve our Data Lake / Lakehouse (Delta Lake) on AWS and / or Azure, from storage layout and security to compute strategy (Databricks, EMR, Synapse) and SLAs.
• Build serious pipelines. Develop batch and streaming ETL / ELT with Apache Spark (PySpark / Scala), Databricks Workflows, and / or Azure Data Factory; integrate sources via APIs, CDC, and message buses (Kinesis / Event Hubs / Kafka).
• Model for analytics & ML. Deliver well-tested models and dimensional / semantic layers for Databricks SQL that power BI and production ML features.
• Engineer for production. Implement observability (custom Spark metrics / listeners, logging, data quality tests, anomaly alerts to Teams), CI / CD (GitHub Actions / Azure Pipelines), infra as code (Terraform), and automated testing (pytest).
• Optimize relentlessly. Right-size clusters, tune Spark jobs, partitioning, caching, and storage formats to cut runtime and reduce cloud spend, and prove it with dashboards.
• Harden governance. Enforce RBAC / row-level security, cataloging / lineage, PII handling, and compliance (e.g., GDPR; HIPAA / FHIR; CCPA / CDPA experience is a plus).
• Partner cross-functionally. Work with analytics, product, and platform teams to define SLAs, own incident response / runbooks, and guide data contract best practices.
• Mentor and lead. Drive design reviews, coach engineers, and set engineering standards for reliability, readability, and long-term maintainability.

What you’ll bring

Nice to have

Our stack (you don’t need all of it)

Databricks, Spark (PySpark / Scala), Delta Lake, Airflow, ADF, dbt, AWS (S3 / EMR / Glue / Kinesis), Azure (ADLS / Synapse / Event Hubs), Terraform, GitHub Actions, Docker, CloudWatch, REST.

What success looks like in 6–12 months