Monthly Notes: Building a data science and engineering team for the social sector

On September 12, Google.org announced that IDinsight is one of 15 organizations receiving its support and?using AI to accelerate?progress toward achieving the SDGs. This project is managed by our Data Science Engineering and Monitoring (DSEM) team. We sat down with Fola Salami, MBA, CISM , who supports DSEM's people operations to hear how he has helped grow a social sector team with private sector expertise.

Q. What are you most proud of since joining IDinsight?

Fola Salami, MBA, CISM , Manager - Research Evaluation and Data team operation:?We’ve managed to (almost) triple the size team in a fiercely competitive market for technical talent and stuck to our organizational values along the way. Our approach was to 1) hire team members who are mission-aligned 2) not compromise on standards of technical excellence, 3) build a team representative of the contexts in which we work, and 4) ensure that the team is diverse.

To paint a picture, we now have 24 team members from nine countries, mostly based out of our hubs in Nairobi and Delhi. More than 70% are regional nationals of places where we work, and 30% of the team is female – while still falling short, it is above industry averages for similar technical teams.

Q. What do you think has contributed to achieving these goals??

Fola:?Many of our social sector partners do not have the in-house expertise or resources to apply big data to solve their most pressing challenges. Those abilities remain exclusive to the private sector in many countries, and I see the DSEM team's work directly filling that gap.

I think it helps that our work connects to a larger purpose: enabling broader access to world-class advanced analytics that would otherwise be inaccessible in the social sector.

Q. What approach did you take to grow the team??

Fola:?We realized, looking at our project pipeline, that the demand for the DSEM team's services would outstrip the current team’s capacity and we would need to expand. In the beginning, the team was comprised of three senior leaders who led the major verticals within the team and a couple of junior engineers. We immediately prioritized building a managerial or middle layer alongside our recruitment drive for more individual contributors.

Over time, as we have learned?more about the demand for various services, we have hired specialists to grow our capacity in some focused areas.

Q. Given the radical focus on impact at IDinsight, how did we decide how much social impact experience was necessary for each role?

Fola:?Unlike other roles at IDinsight, we certainly did not have the benefit of being able to recruit from a pool of candidates with significant sectoral experience, as data science and engineering in the social sector is still relatively nascent.

Rather than using a candidate’s previous experience as a proxy for social impact orientation, this was something that we explicitly sought to test as part of our recruitment process through a combination of screening questions on the application and our interview. So, while everyone did not have previous social impact experience, they were all extremely motivated to have a positive impact.

In fact, a lot of our engineers found us. They reached out because they saw the work we were doing and were excited about using their tech skills for good.?

Q. In hindsight, what do you think worked well?

Fola:?I don’t consider myself a recruiter or a talent acquisition specialist, at least in the traditional sense. Still, there were several tactics that we used that were effective:?

• Focusing on our vision for the team structure: When I look back to many of the conversations I had with prospective team members, many found the vision that we had in place for a mature advanced analytics team very appealing. Joining IDinsight would not mean leaving technology to come and be the sole engineer or scientist at a non-profit – which they saw as a prospect that could stifle their growth despite the remarkable upside in impact potential. I credit much of the thinking around the vision and structure to Ben Brockman, who founded the team - and current leadership - Marc Shotland, Sid Ravinutala, and Eric Dodge.?
• Understanding who we are competing with for talent. Having lived and worked in start-ups in Nairobi (which we chose for our second hub) for many years before IDinsight helped me immensely. For one, I knew many talented people working in tech, but more importantly, I understood the market. That helped us calibrate what we were looking for with what was available. We were more flexible in evaluating things like impact orientation. And sometimes, that looked very different based on people’s backgrounds.
• Establishing beneficial relationships where we have found talented colleagues. We’ve worked with many amazing partners across the continent, such as Zindi, African Institute of Mathematical Sciences, Moringa School, and The ROOM, to understand the African talent landscape. For example, some of our earliest candidates were partner referrals who gave us early insights into candidate archetypes that would later make up most of our organic pipeline.
• Always being open to talent and having zero waste: Here is a secret: we run a rolling recruitment process for most recurring roles on our team. We don’t believe in hard deadlines and stage-by-stage cohort recruitment, We are always open to the best talent regardless of when they enter our pipeline. You know how everywhere you apply for a role unsuccessfully, they tell you they’ll keep your CV on file and alert you if any opportunities that could fit your profile arise? We actually do that! We think about how every role relates to a previous one that we hired for so, every search often begins with an analysis of the pipeline for previous roles. Previous applicants are such an underrated treasure.

Q. Post-recruitment, what challenges have you faced as a team?

Fola:?Having an embedded, fully-fledged Data Science and Engineering team at IDinsight has meant, to some extent, rethinking how we collaborate internally (with client-facing colleagues) and how we work with clients. For example, our traditional project staffing models have evolved to incorporate new DSEM roles, which our client-facing colleagues often grapple with.?

Also, I think, and maybe more importantly, the expertise that the DSEM team brings to IDinsight is not something that many colleagues at IDinsight previously understood and vice versa. Hence, there’s been a need to constantly educate (both ways) on how our work complements each other in the service of our mission to use data and evidence to improve lives.

Q. Finally, what’s next for you and the DSEM team?

Fola:?The reward for all our hard work is that we now have a fantastic team brimming with energy and purpose, and we are constantly on the hunt for the most impactful opportunities that could benefit from our capabilities.

Read the full QA here.

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