From Violin to Code: LinkedIn's REACH Program

A former professional violinist transitions to data science via LinkedIn's REACH program, showcasing career shifts in the tech industry.

May 22 at 1:15 AM6 min read

Javier, a machine learning engineer, smiling and looking towards the camera. — Javier, a machine learning engineer at LinkedIn, found a new career path through the REACH program.· LinkedIn Engineering

Visual TL;DR. Violinist to Data Science leads to LinkedIn's REACH Program. LinkedIn's REACH Program leads to Skill Development. LinkedIn's REACH Program leads to Machine Learning Engineer. Skill Development leads to Machine Learning Engineer. AI/ML Interest leads to Violinist to Data Science. Machine Learning Engineer leads to Refining Recommendation Algorithms. Refining Recommendation Algorithms leads to Measurable Business Gains.

Violinist to Data Science: former professional violinist transitions to tech career
LinkedIn's REACH Program: initiative for diverse professionals bridging tech gap
Skill Development: structured pathway for learning data science skills
Machine Learning Engineer: Javier's new role on Feed AI team
Refining Recommendation Algorithms: working with massive datasets for better user experience
Measurable Business Gains: project work directly contributed to metric improvements
AI/ML Interest: driven by interest in artificial intelligence and machine learning

Visual TL;DRQuickExplainDeeper

Javier, a former professional violinist and composer, found an unexpected second act in data science through LinkedIn's REACH program. This initiative targets individuals with diverse professional histories, aiming to bridge the gap into tech roles.

After a 15-year career in music, Javier pivoted to data science during the pandemic, driven by an interest in AI and machine learning. The REACH apprenticeship provided a structured pathway, offering both skill development and project involvement.

Refining the Member Experience

Now a machine learning engineer on LinkedIn's Feed AI team, Javier works with massive datasets, processing billions of rows to refine recommendation algorithms. He utilizes Python, Scala, and Java for data analysis and machine learning experiments.

His initial project involved experimenting with sampling training data for algorithms, scaling from personal projects to datasets with 500 million rows. This work directly contributed to measurable business metric gains.

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.

Cultivating a Collaborative Culture

LinkedIn fosters a collaborative environment, encouraging engineers to share data and technologies across teams. Javier actively engages with his REACH cohort and founded a data club for apprentices.

His advice for aspiring engineers, particularly those considering a machine learning engineer career change, is to identify and pursue passions, emphasizing the importance of understanding the business and social impact of technology.

This journey underscores the value of transitioning to data science and the opportunities available through programs like REACH for those with unconventional backgrounds.

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