A data science manager is responsible for leading and managing a team of data scientists. They oversee the data science projects and initiatives within an organization to help make data-driven decisions. Some of the key responsibilities of a data science manager include:
Recruiting and Managing Data Scientists
One of the main tasks of a data science manager is to build effective data science teams. This involves recruiting skilled data scientists and other roles like data engineers. A data science manager needs to look for candidates with the right technical capabilities such as programming, statistics, machine learning, and domain expertise.
Besides recruitment, a data science manager also oversees the progress and performance of data scientists. They set goals and expectations for data scientists to achieve. The manager provides feedback and coaching to help them strengthen their skills and grow in their roles. They promote collaboration and knowledge sharing within data teams.
Understanding Business Needs
A data science manager needs to understand the key business goals, challenges, and pain points. They should remain aligned to the overall business strategy and objectives. This enables the data science manager to identify relevant projects and focus areas for the data scientists.
For instance, the business may need data science projects to improve customer retention, reduce churn, optimize marketing spend, detect fraud, and so on. The manager helps prioritize and translate such needs into data science solutions. They serve as the interface between the business stakeholders and the data science team.
Managing Data Science Projects
The data science manager owns the delivery of data science projects within the organization. They work with business leaders and other teams to gather project requirements.
Next, the manager develops project plans and timelines for data science teams to execute on. This includes scoping the required data sources, tools, and techniques. The manager also coordinates with data engineers to build data pipelines and infrastructure.
Throughout the project, the data science manager monitors progress and checkpoints. They escalate any risks or blockers to leadership. Ultimately, the manager is accountable for ensuring projects are completed on time within budget and meet business needs.
Establishing Data Standards and Governance
Since data is a vital asset for data science, an important duty of data science managers is to oversee data governance. They need to ensure proper standards, policies, and controls are applied to data collection, storage, cleaning, processing, analytics, and reporting.
This helps uphold data quality, reliability, privacy, and security within data science programs. It also enables reuse of data and models across projects. Data governance is key to avoiding biased, outdated or illegal use of data.
Advocating for Data Science
Within some organizations, data science may not yet be seen as a strategic capability. In such cases, the data science manager plays a key role to advocate for data science initiatives.
They educate leaders and colleagues about the benefits and applications of data science. The manager identifies quick wins to demonstrate the value of data science and build further buy-in. Over time, they aim to make data science an integral part of decision making.
Managing Relationships with Stakeholders
Data science doesn’t happen in isolation. Data science managers need to foster strong working relationships with various internal and external stakeholders.
Key stakeholders include business leaders, product managers, IT teams, data providers, technology vendors, and more. The data science manager interfaces between the data science team and these stakeholders. They align expectations, resolve conflicts, and enable smooth collaboration.
Staying Updated on Technology Trends
Data science is an emerging field where new tools, techniques, and frameworks are constantly evolving. Data science managers need to stay updated on the latest developments through conferences, online communities, courses, and more.
This allows them to guide their teams on leveraging the most relevant technologies. It also helps shape robust data roadmaps to support innovation in data science capabilities.
Measuring and Communicating Impact
Data science managers should implement processes to track the progress and impact of data science projects. They need to measure key performance indicators relevant to the business such as increase in revenue, decrease in costs, higher conversion rates, and so on.
The manager then communicates the return on investment of data science initiatives to leadership and stakeholders. This reporting helps showcase the tangible value delivered by data science teams. It also makes the case for further investment and expansion of data science capabilities.
Skills Needed
To succeed as a data science manager, some key technical and soft skills are required:
Technical Skills
– Knowledge of data science lifecycle – framing the problem, collecting and cleaning data, applying algorithms, and interpreting results
– Experience with data science techniques like machine learning, predictive modeling, data mining, etc.
– Understanding of data infrastructure including databases, data warehouses, Hadoop, Spark, etc.
– Programming skills in languages like Python, R, SQL, Scala, etc.
– Familiarity with data visualization tools like Tableau, Power BI, etc.
– Expertise in statistical methods and concepts
– Knowledge of analytics tools like SAS, SPSS, MATLAB, etc.
Soft Skills
– Leadership and team management
– Stakeholder management and communication skills
– Strategic thinking
– Project management
– Problem solving
– Business acumen
– Creativity and innovation
– Eye for detail
– Ethics and integrity
Typical Background
Candidates generally require the following background and qualifications to take on a data science manager role:
– Bachelor’s or Master’s degree in computer science, statistics, applied mathematics, or other quantitative fields
– 5+ years of experience doing hands-on data science work – analyzing data, building models, etc.
– 2+ years in a team lead or management role, leading data scientists
– Extensive experience across the data science lifecycle and techniques
– Sound knowledge of data infrastructure and tools
– Strong programming ability in languages like Python and R
– Problem solving aptitude and critical thinking skills
– Leadership, communication, and stakeholder management capabilities
– Experience with data governance and compliance
– Industry/domain knowledge is a plus
Career Path
Here are some common steps professionals take to become data science managers:
– Earn a bachelor’s degree in a quantitative field like statistics, applied math, economics, computer science, or information systems. Complete coursework related to statistics, algorithms, database management, and programming.
– Start career as data analyst or junior data scientist. Gain hands-on experience doing data manipulation, analysis, visualization, and modeling.
– Pick up technical skills in SQL, Python, advanced statistical methods, and machine learning techniques.
– Learn about the data science lifecycle. Work on end-to-end projects from data extraction to model building to insights.
– Develop soft skills in project management, communication, and stakeholder presentation.
– Get promoted to senior data scientist. Take on more complex projects, provide guidance to juniors, interface with stakeholders.
– Transition into a tech lead role. Start leading small teams of data scientists, delegate tasks, review work.
– Move into official data science manager role. Oversee large teams and enterprise-level projects with cross-functional stakeholder engagement.
– Consider getting a master’s degree to advance data science expertise. An MBA can also help build business acumen.
– Continue developing managerial skills in hiring, coaching, strategic planning, budgeting, and leadership.
Salary and Demand
According to Glassdoor, the average salary for a data science manager in the US is $142,529. Senior data science managers can earn $180,000 or more.
Demand for data science managers continues to grow exponentially. As per LinkedIn’s 2020 Emerging Jobs Report, demand for data science managers grew over 650% annually over the last five years.
Data science is a strategic priority across industries to drive competitive advantage. As more companies invest in building data science teams, they also need specialized managers to lead these technical teams. This results in a robust job market for qualified data science managers.
Conclusion
Data science managers play a critical role in helping organizations unlock value from data. They build and lead high-performing data science teams to work on complex analytics projects. Data science managers need technical strength plus business acumen and leadership skills. They collaborate with diverse stakeholders to identify business needs and deliver data-driven solutions. With demand for data science continuing to rise, skilled data science managers will find abundant career opportunities across domains.