Strategies for Building Data Science & AI Teams

Data Science - Sept. 30, 2020 BY ELLEN WEINSTEIN


A number of our clients are considering the balance between outsourcing the data science engineering efforts and building their own data science teams. Whichever approach works for your organization, Xen.AI is here to help you move forward.


There are a number of different options:

1- Long-term plan: Build a Data science team with full-time employees. Hire an analytics lead, a data analyst and a data engineer in that order.

2- Short-term plan: Contract with an independent data scientist who can fill multiple roles and hire for what’s missing.

3- Short-term or Long-term plan: Outsource the work to a AI/ML/Data Science company like Xen.AI, who should be able to provide all the necessary resources immediately and guide you in building a great Data Science team and provide ongoing support.


Let’s look at suggested Data Science team roles and their functionality.


Analytics Lead -This role will be responsible for prioritizing, structuring and framing initiatives based on business needs and managing data team resources. The analytics lead will interface with executive stakeholders to present results that support data driven decisions, process monitoring and operational improvements.


Data Engineer - This role will work with the engineering team to ensure the data science team has access to datasets, servers and tools necessary to do their job. He will be responsible to provide cleaned and transformed data to the data science team.


Machine Learning Engineer - Will combine software engineering with machine modeling skills.


Data Scientist - is a central person of the team. Data scientists play many diverse roles in organizations, with varying levels of business, technical, interpersonal, communication, and domain skills. The Data Scientist will be responsible for determining which business problems can be resolved with data modeling and how. He will apply the modeling and interpret the results. A data scientist is a data and domain expert who has the technical skills to solve complex problems and the curiosity to explore what problems are needed to be solved. A good Data Scientist should have skills of Data and ML Engineers. 


Business Intelligence Analyst - who produces reports and visualizations according to business needs, develops the vision and overall rationale of a company.


Analytics Team -  will carry out all analyses. From a communication to business and task prioritization to producing final scientific models and recommendations to the business.


All team members should be tightly connected to each other and oriented to an overall business success. It is always challenging when you are building a new team to fit into your organization. Many companies try to use their existing engineering team members to develop AI, Machine Learning and Data Science features and models or try to build data science teams. We have seen many instances where these efforts will take time and may slow down the AI feature development speed. Ultimately it can cause end customer dissatisfaction and can even lead to loss of the customer.


Xen.AI's recommendation is to work on building the data science team internally (Long-term plan) and in parallel work with an expert AI team like Xen.AI to advise and meet the short-term AI development needs and to help build the Data Science team internally.


At Xen.AI, we have a great team with AI and domain experts who can guide and help your engineering development teams. We can suggest the best ways to go about it by analyzing where your datasets live across your organization and what initial work needs to be done to prepare for a Data Science team to be successful. We will ensure that we are executing all our projects with lowest possible operating expense in an on-site and offshore delivery model with our globally distributed team.

About the Author

Avatar Ellen Weinstein

VP - Xen.AI

Ellen is a management and business development pro with more than 2 decades of solid experience working with both startups and established businesses in advertising, media, research and technology arenas. Deep understanding of the linear and digital ecosystems. The ability to deliver successful product integration strategies. Demonstrated strengths in national sales and marketing campaigns, with an extensive background in product planning and launch. Proven ability to use in-depth advertising industry knowledge to grow sales and establish key relationships with industry leaders. Skilled in defining and planning large-scale projects including workflow design. Well versed in industry trend analysis and action planning. Known as a proactive problem solver.


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