This is how data science teams can help complete the ML projects.

Most organizations, enabled by a massive increase in the amount and variety of data, are already using data science to understand their business performance and make operational decisions. Some organizations are just getting started with data science, while others have made significant investments and have data science teams spread across global business units. The challenge remains, regardless of your organization's maturity, how to best structure and manage data science teams so they can scale to meet the growing demands of your organization. Most organizations, enabled by a massive increase in the amount and variety of data, are already using data science to understand their business performance and make operational decisions. Some organizations are just getting started with data science, while others have made significant investments and have data science teams spread across global business units. The challenge remains, regardless of your organization's maturity, how to best structure and manage data science teams so they can scale to meet the growing demands of your organization.

Growing the Team

Initially, you may need a small team that mostly works on some analysis or come up with some ideas which you can pitch up to the senior management. But you will soon realize that to build the idea into a product your team needs to have many other skills. The aim should be to grow the data science team into a full product team responsible for designing, implementing, and maintaining products. As a product team, the data science team could experiment, build, and add value directly to the company.

Prioritize Work

It's important to prioritize work and assign the right priority to these Adhoc tasks. Once created an Adhoc requests backlog and added priority to these tasks, the team could then manage these urgent requests better without sacrificing the time towards important tasks.

Data Quality

The first question is: Are you getting the right data? You may have plenty of data available, but the quality of that data isn't a given. To create, validate, and maintain production for high-performing machine learning models, you have to train and validate them using trusted reliable data. You need to check both the accuracy and quality of the data. Accuracy in data labeling measures how close the labeling is to ground truth. Quality in data labeling is about accuracy across the overall dataset. Make sure that the work of all of your annotators looks the same and labeling is consistently accurate across your datasets.

Tools

Tools play an important role because they allow you to automate. You should use relevant tools to do heavy lifting jobs, run scripts to automate queries, and process data to save some time which can, in turn, be used to make the team more productive. The data science team is motivated by solving challenging problems. Automating repetitive weekly reports can help engineers to focus on some new challenging problems. In our team, we made a tool for labeling our data and exposed the tool to the data annotation team. That really helped us to check for data consistency and share the work across different members with a quick turnaround time for the labeling task.

Processes

Data science team projects are research-oriented or start with a lot of research activities, it's difficult to predict how long it will take for them to finish. Also, a lot of activities like model building, data crunching are usually done by a single person, so traditional collaborative workflows don't fit. You have to identify an approach that works best for your team. Like in our case, we run a mix of Kanban and Scrum boards in JIRA. For research activities, data exploration/analysis, exploring ML models go for Kanban mode while as productization of the models you can work as a scrum team. So basically, your data scientists, research scientists, and ML engineers work mostly in Kanban mode whereas data engineers, software engineers work in scrum mode. Evaluate various options and see what best works for your team and projects.