3 Key Responsibilities of Data Science Manager

With rise of data science, machine learning and AI, we are seeing creation of new roles in the industry and data science manager is one of those important roles. Since data science is still a vague term so far, the role of data science manager is not clearly defined. This role would remain evolving until field of data science is matured enough.
At current state of data science field, the role of a data science manager is very challenging yet interesting. As we know data science is quite interdisciplinary field (at intersection of machine learning, statistics and computer science), in business settings a typical data science project may involve different teams: data scientists, business analysts, data analysts, (traditional) project managers, machine learning engineers etc.
The main object of a data science project is always to add value to business by applying machine learning methods to business data. Data science manager is the person who understands the big picture from everyone’s perspective and keeps project on track to achieve its objective.

A data science manager must focus on three tasks - communicate, assist, plan (CAP) - in agile fashion:

1. Communicate Data Science to Everyone:

Data science manager is the face of data science in an organization or enterprise. He must communicate strengths and capabilities of his data science can add to business and hunt for new data science opportunities. He should reach out to potential business partners internally and externally and explore new opportunities and partnerships. Additionally, he must build strong communication channels within his team so that he himself and others in organization can learn state-of-the-art data science in general and capabilities of organization’s data science team in specific.

2. Assist Business and Data Scientists:

Data science manager plays important role to address any data science challenges faced by business and data scientists. From data science perspective, while working on a project both business and data scientists would be having challenges, confusions, questions and uncertainties at many levels such as resourcing, funding, requirements etc. Data science manager keeps eye on bigger picture, and assists relevant teams to address those challenges in an appropriate way.

3. Plan Data Science Strategy:

Data science is an evolving field and is changing at very fast pace. New methods and techniques are being produced every day. Given business challenges of his organization, data science manager is supposed to keep close eye on current state of data science and plan an effective data science strategy: resources, software and hardware infrastructure, technical skills etc. The cornerstone of this strategy should be addition of maximum business value (efficiency, growth, cost reduction) by leveraging business data and science.

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