Research Analyst: ML Analytics for Growth & Decisions

Internetwork Expert; Amsterdam; Hybrid; EUR 70,000 - 100,000; Full time

Benefits offered by this job

High base salary

Generous bonus structure

Tuition reimbursement

Conference sponsorship

Flexible workflow

Job summary

Internetwork Expert is seeking a passionate Research Analyst to optimize a complex production system through advanced analytics and ML. You will tackle challenging problems using statistical analysis and uplift modelling to drive decision-making.

You will design dashboards, run A/B tests, and decompose strategies into measurable components while exploring open-ended questions with probabilistic metrics to improve performance.

Qualifications

• Strong Python skills with pandas, numpy, and PyTorch.
• Solid understanding of probability theory and mathematical statistics.
• Proven experience in data analysis and machine learning.
• Expertise in statistical testing and A/B experiment design.
• Familiarity with ML system design.
• Desire to explore open-ended questions through data analysis.
• Ability to base decisions on probabilistic metrics.

Responsibilities

• Develop and implement performance metrics.
• Identify bottlenecks and opportunities for market share growth.
• Optimize trading constraints and evaluate changes.
• Conduct A/B tests and analyze data.
• Decompose strategies into measurable components.
• Detect anomalies and analyze performance issues.
• Accelerate decision-making by designing analytical dashboards.
• Improve simulation credibility by training dynamic parameteric models.

Skills

Python

Pandas

NumPy

PyTorch

Statistics

Experiment design

Education

Bachelor's or Master in CS/Statistics

Tools

ML System Design

Job description

Internetwork Expert is seeking a passionate Research Analyst to optimize a complex production system through advanced analytics and ML. You will tackle challenging problems using statistical analysis and uplift modelling to drive decision-making.

You will design dashboards, run A/B tests, and decompose strategies into measurable components while exploring open-ended questions with probabilistic metrics to improve performance.