Data Scientist GTM (Go-to-Market)
Openmind Technologies
United States
24 days ago
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Role details
Contract type
Permanent contract
Employment type
Part-time (≤ 32 hours)
Experience level
Expert
Working hours
Regular working hours
Job source
Tech stack
ARM Architecture
Data Mining
Python (Programming Language)
Machine Learning
BIG-IP Global Traffic Manager (GTM)
Jupyter Notebook
Sql Optimization
Snowflake
Build Management
Core Data
Machine Learning Operations
Data Pipelines
Job description
- Maintain, enhance, and troubleshoot existing complex machine learning models in production.
- Partner closely with GTM, marketing, and commercial teams to translate business needs into analytical solutions.
- Build, refine, and operationalize propensity-to-buy, lead-scoring, segmentation, and customer-lifecycle models.
- Work with external data providers to evaluate, integrate, and manage third-party datasets for marketing and sales use cases.
- Develop and automate data science workflows using Python and ML Ops best practices.
- Build and deploy models using Snowflake Cortex ML.
- Write advanced SQL for data extraction, transformation, and analysis.
- Use Jupyter Notebooks for exploratory analysis, prototyping, and documentation.
- Apply core data science and machine learning methodologies to solve GTM and business-driven problems.
Requirements
We are seeking a seasoned Senior Data Scientist for a critical contractor role on our client’s team. The ideal candidate combines strong technical depth with excellent communication skills and has proven experience supporting GTM, marketing, and commercial analytics initiatives. This role requires expertise in engaging with business stakeholders, understanding complex business processes, and collaborating with external data providers., * Strong communication and stakeholder-management skills, especially with GTM, marketing, and sales teams.
- Hands-on experience with propensity modeling, customer segmentation, and marketing analytics.
- Experience with Snowflake Cortex ML.
- Advanced SQL proficiency.
- Strong Python and ML Ops automation experience.
- Expertise with Jupyter Notebooks.
- Deep understanding of data science and machine learning fundamentals.
- Experience working with external data providers and integrating third-party datasets.
- Ability to operate independently in a fast-paced environment with an urgent backfill timeline.
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