Machine Learning Scientist II

Jobtailor; California (MO); On-site; USD 120,000 - 180,000; Full time

Job summary

Jobtailor is seeking an ML Engineer to own the end-to-end lifecycle of machine learning projects, from problem framing to deployment and monitoring. You will design experiments, validate models, and quantify business impact while collaborating with product, engineering, and business stakeholders.

The role emphasizes practical ML, strong Python and SQL skills, data preparation, and a deep understanding of ML theory, including A/B testing and causal inference.

Qualifications

• Bachelor’s degree in Computer Science or a related technical field; or Equivalent related professional experience.
• 1+ years of relevant professional experience.
• Proven ability to design end-to-end ML solutions, including problem formulation, data sources, algorithms, feature engineering, evaluation, deployment and monitoring.
• Strong programming skills in Python and its data science ecosystem (pandas, scikit-learn, PySpark) and proficiency in SQL, with experience following software engineering best practices.
• Familiarity with AI-driven systems and applying AI/ML concepts to real world products, including ML lifecycle from experimentation to monitoring.
• Expertise in applied ML: deep knowledge of ML theory and statistical modeling; strong experimental design and causal inference.

Responsibilities

• Design & Implement ML Solutions: own end-to-end ML lifecycle from ideation to deployment and monitoring.
• Test, Learn, and Iterate: design tests to validate models and quantify business impact.
• Collaborate with product managers, engineers, and business stakeholders to define problems and communicate findings effectively.

Skills

End-To-End ML Solutions

Python Programming

SQL Proficiency

ML Theory

Experiment Design

A/B Testing

Causal Inference

Problem Formulation

Evaluation Strategy

Education

Bachelor’s degree in CS or related field

Tools

Pandas

Scikit-Learn

PySpark

SQL

Job description

Design & Implement ML Solutions: Take ownership of the end-to-end ML lifecycle for your projects, from ideation and research to deployment and monitoring.

Test, Learn, and Iterate: Design and analyze tests to validate your models and quantify their business impact and design future iterations.

Collaborate and Communicate: Partner closely with product managers, engineers, and business stakeholders to understand requirements, define problems, and communicate your findings and results effectively.

Requirements

• Bachelor’s degree in Computer Science or a related technical field; or Equivalent related professional experience.
• 1+ years of relevant professional experience.
• Proven ability to design end-to-end ML solutions, including problem formulation, identification and preparation of data sources, algorithm selection, feature engineering, evaluation strategy, and production deployment and monitoring.
• Strong programming skills in Python and its data science ecosystem (such as pandas, scikit-learn, PySpark) and proficiency in SQL, with experience following software engineering best practices and contributing to shared codebases.
• Familiarity with AI-driven systems, tools, or workflows and applying AI/ML concepts to real world products, including experience with the machine learning software development lifecycle from experimentation through operational monitoring.
• Expertise in applied ML: Deep, practical knowledge of machine learning theory (supervised/unsupervised learning, deep learning) and statistical modeling and a strong command of experimental design (A/B testing) and causal inference to accurately measure impact.
• Able to design end-to-end ML solutions: framing the problem, choosing data sources, selecting algorithms, and defining evaluation strategy.
• Technical Fluency: Strong programming skills in Python and its data science ecosystem (e.g., pandas, scikit-learn, pySpark), plus proficiency in SQL.

Core Competencies

Demonstrates expertise in designing and implementing end-to-end machine learning solutions, including problem formulation, data preparation, algorithm selection, and evaluation strategies. Proficient in Python and SQL, with a strong understanding of machine learning theory and statistical modeling.

Highest-signal resume keywords

• End-To-End ML Solutions
• Python Programming
• SQL Proficiency
• Machine Learning Theory
• Experimental Design

ATS Optimization Keywords

Hard Skills

• Machine Learning
• Data Preparation
• Algorithm Selection
• Feature Engineering
• Statistical Modeling
• A/B Testing
• Causal Inference
• Deep Learning
• Problem Formulation
• Evaluation Strategy

Soft Skills

• Collaboration
• Communication

Industry Keywords

• AI-Driven Systems
• Machine Learning Software Development Lifecycle

Tools & Technologies

• Pandas
• Scikit-Learn
• PySpark