Machine Learning Data Analyst

The Machine Learning Data Analyst will analyze complex data sets, support the product teams in defining KPIs, and develop and implement models. This role requires strong analytical skills to interpret complex data and generate meaningful insights. Expertise in conducting thorough research, designing algorithms, and effectively communicating results is essential to enhance user satisfaction and drive business performance.

Responsibilities:

• Data Analysis and Insights:
• Analyze large, complex data sets to answer business questions and extract insights using SQL. Support the Product team with KPI definition, BI requests, release monitoring, A/B testing, and optimization. Collaborate with the Product team to monitor and understand daily business metrics. Measure and analyze performance to improve user satisfaction and propose revenue-maximizing strategies. Communicate results through clear reports with visual aids.
• Research and Innovation:
• Conduct research from ideation through development. Take part in the entire research cycle: the idea phase, collecting and analyzing data, writing simulations, exploring algorithmic approaches, and building proofs-of-concept. Build and deploy regression, classification, and clustering models. Work with product leaders to implement solutions to optimize user flows, personalize offerings and pricing strategies. Identify opportunities to integrate data from diverse sources. Innovate across teams to drive cross-functional initiatives.
• Model Development:
• Develop and implement advanced models, including ML models. Train models and analyze results to ensure accuracy and effectiveness. Design and apply algorithms based on time series models and machine learning, including anomaly detection.

Bachelor’s degree in statistics, computer science, engineering, or another related field.

3+ years of hands-on experience in data analysis and developing machine learning models.

Technical Skills: Proficiency in SQL, Power BI or equivalent data visualization tools, programming in Python and familiarity with relevant data science tools and libraries. Familiarity with research methodologies, experimental design, and statistical modeling techniques.

Analytical Skills: Strong analytical and critical thinking skills, with the ability to interpret complex data and generate meaningful insights.

Personal Attributes:

• Curious, creative, and impact-driven.
• Self-learner with a strong desire to explore new research areas.
• Team player with a can-do attitude.
• Strong communication and team collaboration skills.