Career Profile: Data Science in Industry

The data science in industry career at a glance

Education: MS or PhD in physics or other scientific or computational field or a BS with relevant skills and experience can be sufficient

Additional training: Experience in programming, machine learning, or working with databases

Salary:

• Starting at $105k - $130k, with mid-career salaries at $150k - $190k for physics PhD’s
• Starting at $80k - $105k for physics bachelor’s, with mid-career salaries at $110k-$150k

Outlook: The private sector employs over half of physics PhDs and about 95% of those with a bachelors in physics. Specifically, data science is a growing field with many job opportunities for physics degree holders.

What they do

A physicist in a data science job will spend most of their time analyzing data and designing and developing models to predict how something will behave based on data of how it has behaved in the past. Data scientists often work with a team to complete projects. Typical activities include:

• Design, develop, and maintain machine learning and other data models
• Select, use, and debug existing data models
• Perform statistical and data analyses, often to make decisions about products or projected audiences
• Conduct research to learn more about the field and to improve model accuracy, including meeting with and interviewing experts
• Work in teams to assess project needs and perform tasks

Some data scientists also work on:

• Data visualization using tools like Python, Tableau, or Power BI
• AI and machine learning model development
• Natural language processing (NLP) tasks like text analysis and chatbots
• Database management and data quality monitoring
• Data infrastructure and pipelines, which consist of all tools and software needed to collect, store, and analyze datasets
• Creating dashboards or data-driven web apps (e.g., using Streamlit, Dash, or deploying ML models via APIs like Flask/FastAPI).
• Communication with multiple teams, such as marketing, technical, etc.

Education & background

A bachelors in physics or other scientific/computational field can be sufficient, but a masters or PhD in these fields is often preferred. Programming skills and familiarity with machine learning, databases, and statistics are critical.

Commonly used languages in data science include: Python (including packages: pandas - numpy - scikit-learn - matplotlib/seaborn - tensorflow/pytorch), SQL, and R. Proficiency in these languages enhances a candidate's appeal. Additionally, familiarity with cloud platforms (such as AWS, Google Cloud, or Azure) and machine learning frameworks (like TensorFlow or PyTorch) is increasingly valuable in the industry.

Unlike many academic positions, experience in postdoctoral appointments is not considered a prerequisite for data science jobs in most private sector companies.

Additional training

Technical experience in the following can better prepare candidates for data science jobs and increase chances of hire:

• Machine learning projects (like participating in a competition like Kaggle.com, an online machine learning resource hosted by Google)
• Python (including packages: pandas - numpy - scikit-learn - matplotlib/seaborn - tensorflow/pytorch)
• SQL and database experience with large datasets
• Familiarity with AI and ML frameworks and staying updated on advancements in AI-driven tools and automation can significantly boost one’s job prospects.
• Internship in data science or related field

To build and enhance data science skills, consider exploring platforms like Coursera, edX, and DataCamp for courses in machine learning, data analysis, and cloud computing. Participating in competitions on Kaggle can provide practical experience, while practicing coding problems on LeetCode can prepare you for technical interviews. The book Cracking the Coding Interview can also be a helpful guide when preparing for job interviews. The field is evolving rapidly and AI tools like GitHub copilot and other GenAI tools are becoming more common for coding assistance. This article discusses the value humans can bring to coding in the age of AI.

Beyond technical expertise, data scientists must possess strong communication and collaboration skills. The ability to convey complex data insights to non-technical stakeholders, being able to tell a story with data visualization, and working effectively within diverse teams is crucial for success in the industry. Additionally, actively listening to colleagues and being able to interpret technical goals can be just as important. Effective communication and collaboration skills can set candidates apart. Students and early career physicists often have translatable skills, such as experience working in scientific collaborations or giving talks at conferences. Additionally, to succeed in industry, one has to be flexible in changing projects and willing to learn new skills. A defining characteristic of jobs in industry is that things move quickly; being able to work efficiently on projects and meet deadlines is key.

When applying for a job in the private sector, understanding the difference between a CV and a resume and being able to write a good resume are very important. For a good tutorial on the difference between CVs and resumes, and for advice on how to write a skills based resume suitable for private sector jobs, please watch our video tutorial.

Career path

Most physicists will start out as a data scientist or analyst, spending a majority of their time writing/developing code. After working for about 5 years or so as an individual contributor, some will move into more senior positions, choosing either management roles or continuing to be an individual contributor as a senior data scientist. Other options include being a technical leader or architect, or a manager. In such a role, a data scientist would spend most of their time on project, resource and personnel management. High level management positions in companies carry among the highest salaries for physicists in the private sector.