The over-reliance on data scientists is increasing to extract meaningful insights from large datasets to make informed decisions, predict future trends, or even optimize business operations. Lead data scientist is the senior position whose role revolves around developing and deploying effective data science techniques to solve business problems.
The top industries that are recruiting lead data scientists in the USA are companies and enterprises, scientific research and development services, depository credit intermediation, technical consulting services, and many more. There are more than 285 jobs for lead data scientists opened in the last month (Glassdoor).
Curious to know more about lead data scientists?
Then this blog is for you! This blog will discuss the primary roles and responsibilities of a lead data scientist in an organization. Additionally, you will also discover what certifications you need to find high-paying jobs for lead data scientists.
Primary Responsibilities of Lead Data Scientists
One of the key responsibilities of a lead data scientist is to oversee the end-to-end lifecycle involved in the entire data analysis process to ensure there are no errors. Here is a list of their key responsibilities and roles to give you a better idea:
- Management of the entire data science team, building analytical models, and planning projects by leveraging the large volume of data.
- Implement data mining procedures and ensure they are error-free.
- Ensure data veracity and data quality.
- Perform data modeling and test the performance of data-driven solutions.
- Work on data visualization and deriving reports.
- Aligning the data project according to the business objectives.
What salaries can you expect?
As this is a senior position, professionals are expected to develop strong technical expertise, leadership abilities, and mentor or train juniors. As a result, the demand and salary for this job position are increasing drastically across sectors worldwide.
According to Indeed, the average salary withdrawn by a lead data scientist range from $162,955 to $164,469 a year. However, the accurate salary estimate depends on a range of factors, including previous work experience, location, etc. As per the report shared by Indeed, some of the highest-paying cities for lead data scientists in the USA are:
- Santa Clara, CA – $233,563 a year
- San Francisco, CS – $232,667 a year
- Boston, MA – $208,467 a year
- McLean, VA – $174,270 a year
- Minneapolis, MN – $168,836 a year
Skills needed to become a Lead Data Scientist
To excel in the field of data science, individuals are required to develop both technical and non-technical skills. These skills are critical to processing data, building models, and communicating meaningful insights.
Technical Skills –
- Programming expertise in R, Java, and Python to perform data manipulation and analysis.
- Data manipulation techniques in Pandas, SQL, and NumPy are required to handle and query data.
- Knowledge of algorithms and frameworks such as PyTorch, TensorFlow, Scikit-learn, etc., is useful for data modeling.
- Data visualization ability is crucial to presenting data to stakeholders using tools like Power BI, Tableau, Seaborn, etc.
Non-technical or Soft Skills –
- Problem-solving and critical thinking abilities are crucial to designing data-driven solutions for solving business problems.
- Explaining research findings to non-tech stakeholders is equally important for a lead data scientist.
- Having a good understanding of the business and developing solutions accordingly is one of the key responsibilities of a lead data scientist.
- Collaboration with cross-functional teams like product managers, business analysts, and engineers is useful for working effectively.
How can you master both skills?
If you are planning to master both technical and soft skills, then pursue a full-time data science certification from any reputed institution and boost your credibility. Here are some options for you:
I. Massachusetts Institute of Technology (MIT) – Data Science and Machine Learning Certificate
Summary:
This is a professional program that emphasizes theory along with real-world applications, thereby giving learners a complete understanding of the subject.
Offered under the MIT Professional Education, learners will be taught topics like predictive modeling, big data analytics, deep learning, and Python programming.
With a strong focus on industry use cases, this certification prepares students to be ready for the job market.
II. United States Data Science Institute (USDSI®) – Certified Lead Data Scientist (CLDS™)
Summary:
Ideal for senior executives, data scientists, or other tech professionals, this program stands out thanks to its industry-paced learning and customized learning resources like Study Book 1, Study Book 2, and Study Book 3. All these eBooks cover advanced data science and relevant topics like data analytics, machine learning, deep learning, NLP, etc. Additionally, through real-world use cases, this certification helps learners prepare for leadership roles and find high-paying jobs as lead data scientists.
Unlike traditional programs, this is offered in a self-paced learning environment, making it easy for learners to complete the program at their own pace on the go.
As it is valued by top industry recruiters because of its robust futuristic curriculum and industry-relevant learning practices, possessing the certification will help you be more credible and respected among employers worldwide.
III. Technical University of Munich (TUM) – Data Engineering and Analytics Certificate
This is a specialized program for professionals who have technical knowledge and are interested in learning computational statistics, big data, etc. Offered by TUM, this certification has a strong focus on practical applications to help you extract meaningful insights from large datasets. You will also find case studies from top German industries, which will further enhance your domain expertise.
Conclusion
Overall, a lead data scientist is not just required to have strong technical expertise but also problem-solving abilities and strategic abilities to bridge the gap between data and business success. In this ever-evolving technological environment, professionals are required to stay updated with the trends and develop leadership qualities, a forward-thinking approach combined with technical knowledge to stay ahead of the competition. If you are aiming to pursue a career as a lead data scientist, then it is recommended to keep upskilling in trending techniques and software or opt for a full-time certification from an accredited institute like those mentioned above.
