Javier, a former professional violinist and composer, discovered an unexpected second career in data science through LinkedIn’s REACH program. This initiative targets individuals from diverse work backgrounds and aims to bridge the gap to technical careers.
Visual TL;DR. A path from violinist to data science led to LinkedIn’s REACH program. LinkedIn’s REACH program leads to skill development. LinkedIn’s REACH program connects machine learning engineers. Skill development leads to machine learning engineer. My interest in AI/ML leads me from being a violinist to data science. Machine learning engineers lead to the refinement of recommendation algorithms. Refinement of recommendation algorithms provides tangible business benefits.
From violinist to data science: A former professional violinist transitions to a career in technology
LinkedIn’s REACH Program: An initiative for diverse professionals to bridge the technology gap
Skills Development: A structured pathway to learning data science skills
Machine Learning Engineer: Javier’s new role on the Feed AI team
Refining Recommendation Algorithms: Working with Large Datasets to Improve User Experience
Tangible business benefits: Project work directly contributed to improved metrics
Interest in AI/ML: Driven by interest in artificial intelligence and machine learning
Visual TL;DR
After a 15-year career in the music industry, Javier pivoted to data science during the pandemic due to his interest in AI and machine learning. The REACH apprenticeship provided a structured pathway that provided both skills development and project participation.
Improving member experience
Currently a machine learning engineer on LinkedIn’s Feed AI team, Javier works with large datasets, processing billions of rows to refine recommendation algorithms. He utilizes Python, Scala, and Java for data analysis and machine learning experiments.
His first projects involved sampling training data for algorithms that scaled from personal projects to datasets of 500 million rows. This initiative directly contributed to improving tangible business metrics.
Javier also participates in on-call shifts, managing the global LinkedIn feed and making critical, split-second decisions to ensure a seamless member experience for millions of people.
foster a collaborative culture
LinkedIn fosters a collaborative environment and encourages engineers to share data and technology across teams. Javier is actively involved with REACH colleagues and established a data club for trainees.
His advice for aspiring engineers, especially those considering a career change to become a machine learning engineer, is to identify and pursue your passion, and emphasizes the importance of understanding the business and social impact of technology.
This initiative highlights the value of transitioning into data science and the opportunities available through programs like REACH for those from unconventional backgrounds.
