Senior Data Scientist - Product Analytics & AI Automation

Intelliswift - An LTTS Company; Menlo Park (CA); On-site; USD 120,000 - 150,000; Full time

Job summary

Intelliswift - An LTTS Company is looking for a seasoned Data Scientist in Menlo Park, California. The role involves applying advanced statistical analysis, machine learning, and building predictive models. Ideal candidates will have over 10 years of experience in data science, proficient in Python and R, and a Master's degree in a relevant quantitative field. The position requires strong analytical skills and the ability to communicate complex concepts effectively. Collaborative work with engineering and product teams is essential to translate prototypes into scalable features.

Qualifications

• 10+ years of experience in data science, analytics, or a related field.
• Strong expertise in Python and/or R for data analysis and modeling.
• Experience working with large-scale datasets and distributed data systems.

Responsibilities

• Apply advanced statistical analysis, machine learning, and mathematical modeling.
• Design, build, and evaluate predictive models and algorithms.
• Develop and test hypotheses; analyze results from experiments.

Skills

Python

R

Machine learning

Statistical modeling

Data analysis

Data visualization

Problem-solving

Education

Master’s degree in Computer Science

Master’s degree in Data Science

Master’s degree in Statistics

Tools

Tableau

A/B testing

Job description

Intelliswift - An LTTS Company is looking for a seasoned Data Scientist in Menlo Park, California. The role involves applying advanced statistical analysis, machine learning, and building predictive models. Ideal candidates will have over 10 years of experience in data science, proficient in Python and R, and a Master's degree in a relevant quantitative field. The position requires strong analytical skills and the ability to communicate complex concepts effectively. Collaborative work with engineering and product teams is essential to translate prototypes into scalable features.