Dextro Analytics decode business problems and optimize decisions in marketing, product innovations and operations. It helps to make an ecosystem of adaptive decision making through analytic sensors, Machine Learning, and Artificial Intelligence. It makes use of Decision Engineering Process to solve complex process.

Ajith Govind, Founder of Dextro Analytics had a strong vision to start the company having years of experience in the field of analytics. In an interview he shares his experiences and few words about the company and also his expectation from the talent pool of India.

1. What motivated you to start a company in Analytics?

Both I and Manmit, Co-founder, come from analytics consulting world. We knew exactly where our clients were facing issues and where there were unmet needs in the market. There is a lot of hype going on in the analytics world, but still only 5-10% work actually translates to real impact. We wanted to change that by creating a niche product built for specific use cases. Over the years, we have evolved completely into a product company with major focus on CPG companies, with 3 of the Top 5 CPG companies as client partners today.

2. What are the unique challenges they find when hiring entry level talent in analytics?

Analytics has multiple divisions like predictive, prescriptive, and descriptive and an individual has to figure out which part to go, which doesn’t happen all the time. Most of the time this happens from pure luck on project assignment, and they remain confused in the choice. So, this is the first challenge that we come across. Secondly, these areas keep changing in terms of functions so people find it hard to adapt sometimes. Finally, the kind of work we do, and our business model needs experienced hires with previous industry knowledge. We do think learning is good but it’s always faster and better to learn given that you have context. The last thing that we want to do is for our clients to invest time and effort in training us.

3. How do you overcome these challenges?

We give internship opportunities to the freshers so that they become deployable fast. We have had good success with internships. We select only 2-3 interns and they are sent on-site to work directly with our on-site and shadow them. This gives them a great opportunity to learn advanced analytics along with the real flavor of business knowledge.

4. Do you hire on the basis of certifications and trainings?

Certifications and trainings can help but it must be justifiable. We look for people on the basis of how good they are with their skill set.

5. Do you currently hire talent at the entry level?

No, currently we are not hiring freshers because a lot of challenges are being faced in this process. Also, we give a lot of emphasis to folks who have the right mix of skills and experience.

6. What are your expectations in terms of technical and soft skills from entry level talent?

First of all they should be really good at coding. They should have good grip on algorithms, concepts of Machine Learning and Deep Learning. There should be understanding of Statistics, Modeling and, having an experience with projects involving real life problem solving. They need to attempt as many real life case studies as possible.

7. Do you believe that non engineers can perform well in analytics?

We generally hire individuals having Computer Science background. But I believe even non engineers can perform well in analytics if they are good at mathematics and, have a bit of coding skills. Above all, the right attitude plays a big role.

8. Can you share about an interesting project of your organization?

There are a lot of interesting projects which happened till date. One of them is understanding how CPG companies can reduce cost by simulating lab and consumer tests. We were able to save around $ 8 Million USD for one of our CPG clients last year.

Another one is understanding consumer behavior using video analytics. For e.g.- If a consumer is brushing teeth, we can know how much time is spend in the process or how much water used and so on. This can then be used for product improvements.