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Senior Data Scientist ML Engineer at HP Law
Spring, Texas, United States -
Full Time

Start Date

Immediate

Expiry Date

16 Feb, 26

Salary

0.0

Posted On

18 Nov, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Machine Learning, Deep Learning, MLOps, Data Engineering, ETL, ELT, Python, SQL, Databricks, Apache Spark, REST APIs, GraphQL, A/B Testing, Statistical Analysis, Git, CI/CD

Industry

IT Services and IT Consulting

Description
Design, train, and deploy machine learning and deep learning models, including propensity models, recommendation engines, and customer behavior prediction systems. Own the full ML lifecycle—from feature development through training, evaluation, deployment, and ongoing model monitoring using scalable MLOps pipelines. Collaborate with data engineering and business teams to operationalize insights and ML models. Design and maintain large-scale ETL/ELT data workflows and integrate structured/unstructured data. Develop and integrate with REST and GraphQL APIs for data ingestion and ML-driven services. Leverage Python, SQL, Databricks and Apache Spark for data exploration, mining, cleansing and transformation. Conduct A/B testing, statistical analysis, and experimentation to improve engagement and business KPIs. Implement secure coding practices and leverage Git, CI/CD, and automated testing. Bachelor's or Master's in CS, Data Science, Engineering, Statistics, or related field. 7-10 years in data science, ML engineering, or data engineering roles. Proficiency in Python, SQL, ML frameworks, and distributed data processing (Spark, Databricks). Experience with AWS and Azure. Strong ETL/ELT skills and experience with large-scale datasets. Experience with REST/GraphQL APIs and third-party API integration. Strong understanding of Git, CI/CD, and production-grade ML systems.

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Responsibilities
Design, train, and deploy machine learning and deep learning models while owning the full ML lifecycle. Collaborate with data engineering and business teams to operationalize insights and maintain large-scale ETL/ELT data workflows.