Sr. Data Scientist (Contract)

UP.Labs-posted about 2 months ago
Mid Level
Los Angeles, CA
1-10 employees
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We’re partnering with a global energy industry leader to build an AI-driven intelligence platform that turns complexity into clarity — enabling organizations to understand change faster, act decisively, and unlock new levels of strategic advantage. This is a rare opportunity to work shoulder-to-shoulder with a global market leader, tackling high-impact problems with cutting-edge technology and taking bold ideas from 0 to 1. As Sr. Data Scientist, you’ll be at the core of this effort—designing, training, and deploying models that turn complex, messy, real-world data into actionable intelligence. You’ll work closely with product, engineering, and domain experts to prototype, validate, and productionize AI solutions that directly impact how our users plan, produce, and operate. This is a hands-on role for a builder who thrives in ambiguity, moves fast from idea to experiment, and loves turning data into real-world results. Please note this is a 12 month contract (1099). Extension possibility TBD.

  • Design and implement ML models for prediction, optimization, and pattern detection across large, multi-source industrial datasets.
  • Collaborate cross-functionally with product managers, engineers, and data engineers to define use cases, collect requirements, and translate business needs into quantitative problems.
  • Build and maintain data pipelines that support scalable model training, evaluation, and deployment.
  • Prototype rapidly—exploring different modeling approaches (e.g., regression, time-series forecasting, clustering, reinforcement learning, and deep learning) to find the most effective solutions.
  • Evaluate and improve model performance through rigorous experimentation, feature engineering, and error analysis.
  • Partner with the AI/LLM engineering team to integrate predictive and generative components into end-user workflows.
  • Communicate findings clearly to both technical and non-technical audiences—bridging the gap between model outputs and business decisions.
  • Establish best practices for reproducibility, versioning, and documentation to support long-term scalability.
  • 6+ years of professional experience in applied data science, machine learning, or advanced analytics.
  • Proven ability to build and deploy models in production, ideally within a B2B SaaS or enterprise environment.
  • Expertise in Python, SQL, and common ML frameworks (scikit-learn, PyTorch, TensorFlow, XGBoost, etc.).
  • Strong experience with data wrangling, feature engineering, and exploratory data analysis using large-scale structured and unstructured datasets.
  • Ability to design experiments and A/B tests, interpret results, and guide decision-making through data.
  • Strong communication and storytelling skills—able to contextualize technical insights for executives and business stakeholders.
  • Familiarity with time-series, forecasting, and predictive maintenance models is a plus.
  • Experience with cloud-based ML pipelines (GCP, AWS, or Azure) and data infrastructure tools (Databricks, Spark, Airflow, etc.).
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