Data Scientists Career Path: From Associate to Director Levels

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Data Scientists Career Path: From Associate to Director Levels

Data scientists are high in demand as almost all organizations are becoming more oriented toward using data for business. Learn the data scientist career path here.

· Big Data Zone ·
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It’s a data world now. Given the astronomical rise of data being churned out every day, businesses everywhere are interested to glean insights from their extensive data. They are becoming more reliant on data scientists to create business value from data collected.

This has led to an immediate spike in demand for data scientists. A data scientist is a relatively new career trajectory, where organizations hire them at various levels as junior, mid-level, senior, principal data scientist, and director.

If you are new to the data science career with an aspiration to become a data scientist, know how to tread your data scientist path from level 1 to moving up high as the Director.

Data Scientist Career Path

A data scientist is expected to have skills and knowledge in Data Science, Statistics, and Engineering. The typical path followed by a data scientist is as designed below:

Associate/Junior Data Scientist: Level 1.0

Being a Junior/Associate data scientist, you are expected to test new ideas, debug, and refactor existing models. You act as a great team player when you can pitch new ideas, take responsibility to improve code quality and impact.

If becoming a data science professional is your destination career, then you can start before graduation by becoming proficient in programming languages like Python, Java, R, and SQL/MySQL while refreshing your knowledge in Applied Mathematics and Statistics.

Early exposure to the field would be a good head start and also helps to determine whether a data science career fits your interest. The most sought-after subjects in your graduation would be Computer Science, Information Technology, Mathematics, Statistics, and Data Science.

You should be skilled in data science, machine learning, Python, R, research, SQL, data analysis, analytical skills, teamwork, and communication skills.

Data Scientists Mid-Level-I Roles: Level 2.0 

After gaining work experience of one-three years, you can level up your career to Senior Data Scientist or Machine Learning and AI Engineer, if AI specialization interests you more. At this stage, organizations prefer certified data scientists than non-certified professionals. So, it is recommended to earn one or two relevant data science certifications.

Senior Data Scientist

Being a senior data scientist, you are expected to build well-architected products. Senior data scientists generally avoid greenhorn mistakes, avoids logical flaws in the models, revisits high-performing systems, write reusable code, builds resilient data pipelines in the cloud environment, and prepares immaculate data. They are also capable of mentoring Associates and answer business questions to higher authorities and management.

In addition to a Master's degree, many might have completed a Ph.D. and hold a senior data scientist certification.

AI/Machine Learning Engineer

Data scientists must leverage Machine Learning and Artificial Intelligence (AI), the burgeoning fields. Machine learning has become the core of the mission of an organization. So, it is necessary for the existing and upcoming data scientists must adopt machine learning solutions end-to-end. You must be able to design, create, evaluate, or deploy models to production, monitor, and logging of the decisions, and visualize data.

It is necessary to get acquainted with knowledge and skills like Artificial Intelligence, Deep Learning, Machine Learning, Natural Language Processing, Data Science, Python, C++, SQL, Java, and software engineering. It is highly appreciable to earn Machine Learning or Artificial Engineering certifications apart from holding the best data scientist certifications.

Data Scientists Mid-Level-II Roles: Level 3.0 

In this middle tier, soft skills become much important. One should be tech-savvy and business-savvy as well, should understand business, different types of data analytics technologies, apply methods to valuable authentication, prevent frauds, and maintain budgets. It is recommended to understand parallelization, scalability, and complexity analysis. It is crucial to shape data products in alignment with corporate strategy and provide data insights leading to business directions.

Principal Data Scientist

Principal Data Scientist is the most experienced member of the data science team with 5+ years of experience and is well-versed in data science models. They will be lurking around high-impact business projects. Most of them have a Ph.D. and principal data scientist certification. A principal data scientist (PDS) works with a mission to leverage their strength in machine learning, take the lead to provide strategic direction at scale.

It is expected to understand challenges in multiple business domains, discover new business opportunities, and leadership excellence in data science methodologies. Further, they have scientific and industrial maturity while delivering designs and algorithms to make and quantify cross-organization trade-offs.

Also, PDS plays a significant role in developing other juniors, act as a technical consultant to product managers, and valued as an asset to any data science projects.

Data Science Manager/Architect

This is another high position in data science where the professional has a combined knowledge in database systems and programming languages. They lead the team, set priorities for the team, and communicate findings to the management.

Most of them possess certifications like Microsoft Certified Professional, Certified Analytics Professional, or SAS/SQL certified practitioner qualifications as per the organization’s practice they are working. As the role is more of team leadership and project management, a Master’s in business administration is best recommended.

Data Scientists Advanced-Level Roles: Level 4.0

To climb to this level, one must demonstrate an ability to guide teams, oversee strategic data analysis, aware of the latest technologies. To run an organization’s entire data science operations is rewarding with the right combination of skills. The decisions made by the director determines the organization’s success or failure.

Key Takeaways

Navigating the data scientist career path is fun, challenging, fascinating, interesting, and rewarding. Gain good knowledge to become the best associate. Be ready to implement models into production to become a senior. Level up, evaluate your skills, add on outstanding skills, and dare to make data work for you and your organization.

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Published at DZone with permission of Niti Sharma . See the original article here.

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