Building a Remote Data Science Center of Excellence

The Advantages of a Remote Data Science Center of Excellence

As data becomes an increasingly vital asset, many companies are investing in building out data science capabilities. Establishing a remote Center of Excellence (CoE) is an efficient way to build a talented, agile data science team. Here are some of the key benefits of creating a remote Data Science CoE.

Access to Specialized Talent and Skills

A remote CoE model gives you access to data scientists, engineers, and analysts around the world. This is especially helpful for niche or cutting-edge skills that may not exist locally. The ability to recruit globally allows you to build a world-class team of data experts regardless of geography.

Location Flexibility

Having a remote CoE enables you to hire the very best data talent without requiring them to relocate. Data scientists can work from wherever they have the best quality of life and personal support systems. Location flexibility also allows you to scale up rapidly by leveraging talent pools across different regions.

Improved Diversity

Since you can recruit from anywhere in the world, remote CoEs are far more likely to yield a diverse team than traditional colocated models. Diversity - in background, ethnicity, gender and thought - leads to richer insights and more innovative solutions. A globally distributed team better reflects the diversity of a global customer base.

Cost Efficiency

Operating a virtual Data Science CoE can result in major cost savings compared to an in-house team in a high cost technology hub. Real estate, equipment and other infrastructure costs are minimized with a distributed remote setup. Lower geographic cost of living for some team members also reduces salary overheads.

Productivity Focus

Working from home eliminates commutes and other distractions inherent in office environments. Data scientists can focus more deeply on solving complex problems without constant interruptions from colleagues. Fewer meetings and administrative tasks leads to greater code output and higher productivity levels.

Data Security

With a fully remote CoE, sensitive customer data does not need to be copied and stored on company premises. Client data can stay in its original secured environment and accessed virtually by the analytics team. This reduces security risks of data proliferation and physical theft from centralized corporate servers.

Business Continuity

The COVID pandemic highlighted the risk of colocated teams when offices shut down. A distributed model where data experts work remotely across multiple geographies minimizes the chance of the entire CoE grinding to a halt if any one location is affected. This built-in redundancy enables continuity.

Scalability and Agility

It’s easier to scale a remote Data Science CoE by sourcing new team members from talent pools globally. Experts can be added to match spikes in project demand. Conversely, contractors can be engaged on a short term basis for temporary initiatives without long term overhead. The elastic workforce provides flexibility and agility.

Reduced Corporate Footprint

With data scientists working from personal home offices across different time zones, real estate needs are minimized. Some small regional hubs may supplement virtual collaboration, but the overall facilities footprint is greatly reduced compared to a centralized office-based CoE model.

Data Science Focus

Allowing data experts to work remotely avoids getting pulled into non-core responsibilities like company events, office administration and other on-site meeting demands. The virtual CoE structure enables everyone to devote work hours fully to data science initiatives rather than ancillary activities.

Improved Work-Life Balance

The flexibility to manage their own schedules without commuting leads to higher morale and work-life balance. Data scientists can shift hours to accommodate personal needs and caregiving without productivity suffering. Work-from-home eliminates stressful, tiring commutes which takes a toll over time.

Access to Shared Knowledge

Using collaboration tools like Slack, Zoom and GitHub enables continuous knowledge sharing and transparency across the distributed CoE team. Best practices, code, templates and lessons learned can be easily pooled and accessed by all team members regardless of geography.

In summary

A remote data science CoE provides access to the best global talent, lower costs, higher productivity, built-in redundancy, and greater agility. The flexibility to hire and engage specialists anywhere allows you to scale up niche expertise that may not exist locally. Eliminating commutes and office distractions allows your data experts to provide full focus on solving complex analytical problems. For data science initiatives that require top skills and optimal productivity, a remote Center of Excellence is hard to beat.

Random Forest Services specialize in setting up remote data science centers of excellence at low cost locations. Contact us to learn more.

Remote data science center