💻 Retaining Data Scientists - 4 Key Ways!

DTP #23: Plus, how GenAI can transform HR

“it's not just about what can I do (for my team) over the next month. We need to think ahead. It has to be long term consistent planning.”  

Retaining data science talent requires a strategic blend of professional growth opportunities, competitive compensation, and a supportive work environment. 

We’ve compiled some of the key insights from our conversation with Conor McNeilly last week, plus, we expanded on how you can implement them for data science talent retention. 

Read on! 

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🌐 From the Web

The demand for AI talent is high, but the supply is limited. To attract and retain AI professionals, companies should focus on acquiring skills rather than predefined roles, understand AI workers' preferences and tap into hidden talent pools. 

To address the shortage of data science and machine learning talent, Chief Data and Analytics Officers must lead upskilling initiatives, to raise machine learning literacy and encourage collaboration with data scientists, foster interconnected data science communities, and design upskilling roadmaps. 

Advances in technology are rapidly changing the skills required in the workforce, with the half-life of skills shrinking to under five years. Reskilling is emerging as a strategic imperative, enabling companies to build competitive advantage by filling skills gaps. 

👩‍💻 Be an Open Book about Career Growth

“There are definitely instances where, particularly [if a] business isn't growing massively, finding that opportunity, especially if someone wants to be a person manager, to move upward can be difficult. I do think [we] have to keep [them] reassured that there will be a space” 

  1. Career advancement opportunities can be challenging, especially in slower-growing businesses, particularly for those who aspire to be managers. 

  2. Reassuring employees about future career opportunities, even if not immediate, is essential. 

  3. Employees can focus on acquiring new skills and experiences while waiting for higher job titles to become available. 

  4. The emphasis should be on continuous personal and professional growth. 

  5. Offering options for exploring other opportunities within or outside the organization can be a solution to career advancement limitations. 

💰 Re-evaluate Data Scientist Compensation 

Traditional pay structures mainly focus on acquisition. There needs to be shift the focus towards growth in remuneration, particularly for retaining talent.

This would involve incorporating a growth perspective, such as skillset allowances and progressive bonuses. Understanding the preferences of millennials is also crucial, as they tend to plan for the short term, emphasizing the importance of retaining employees for 2-3 years.  

Additionally, the unreliability of salary benchmarks in emerging analytics job markets means it’s necessary for a broader approach to incentives beyond just data scientists. Connecting capability development with remuneration, including skillset allowances, is important to enhance employee retention and recognize certifications for data professionals. 

🚀 Offer Opportunities for Personal Development 

“It is also important to have the right platforms available to people. Especially when it comes to technical development, data science, analytics, so having the likes of data camp [or] other sources, training people to make good use of [platforms] like Stack Overflow even when it comes to their own technical development.” 

Making space within the business context for personal development is crucial: 

  1. Learning and Development Stipends: Providing financial support for data scientists to attend conferences, workshops, or enroll in relevant courses to enhance their skills and knowledge. 

  2. Career Coaching: Offering guidance and coaching to help data scientists navigate their career paths within the organization and set meaningful goals. 

  3. Mentorship: Pairing data scientists with experienced mentors who can provide insights, advice, and knowledge sharing. 

  4. Continuing Education: Encouraging and supporting data scientists in pursuing advanced degrees or certifications in data science or related fields. 

  5. Innovative Projects: Assigning data scientists to cutting-edge projects and technologies that challenge their abilities and keep them engaged. Working on these projects allows them to apply new tools and techniques to solve real business problems, making a significant impact on the company's success. 

⚖ Set Preventative Measures for Burnout 

Due to the high demand for data science expertise, organizations might be tempted to burden their data scientists with an excessive workload, tight timelines, and extended work hours. Nevertheless, a failure to establish a healthy work-life balance is a fast track to burnout. 

At the very least, companies should provide flexible work hours and guarantee that their employees take sufficient paid time off. Moreover, they can proactively address burnout by regularly arranging team-building events, consistently acknowledging employee contributions, and conducting periodic check-ins with managers. 

Nonetheless, by offering support to your data scientists and ensuring they engage in meaningful, cutting-edge, and purpose-driven projects, you can secure their long-term success within your organization. 

💼 AI in Business

GenAI’s potential impact in HR

Image generated by Midjourney

An article from Boston Consulting Group, examines how GenAI is driving accelerated engagement of HR with artificial intelligence, offering powerful capabilities for the discipline. 

Key Points: 

  • It transforms HR into a more strategic function by increasing self-service, enhancing productivity and employee experiences, and delivering personalized HR services. 

  • GenAI fosters interconnected data science communities and enables a skills-based talent ecosystem linked to the company's workforce strategy. 

  • HR leaders can choose between cost savings and talent effectiveness by deploying GenAI, potentially increasing HR productivity by up to 30%. 

  • HR should be cautious about GenAI's risks and work closely with legal and business leaders to ensure responsible AI implementation. 

  • HR's adoption of GenAI can set an example for the rest of the business to engage with this transformative technology. 

  • HR leaders must help drive broader organizational change as GenAI brings new capabilities and objectives within reach. 

💬 Social Highlight

  • Data Scientists talk about their challenges with management: Link

  • OpenAI offers reward for identifying AI risks: Link  

  • How price optimization works (Illustrated with data): Link 

💡 Word From Our Data Scientists

Learn more about TeamEpic’s Data Talent here.

🤖 Prompt of the week

“Create an onboarding evaluation survey with 10 questions that the employee will get after their first 30 days.” 

See you next week,

Mukundan.

Do you have a unique perspective on developing and managing data science and AI talent? We want to hear from you! Reach out to us by replying to this email. 

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