What Does a Data Scientist Do?
A data scientist analyzes and interprets complex datasets to help organizations make strategic decisions. They use a mix of statistical techniques, machine learning algorithms, and programming to uncover patterns, predict outcomes, and identify actionable insights. Their work supports product development, customer experience, operational efficiency, and revenue growth.
Data scientists don’t just write models; they frame business questions, test hypotheses, clean data, and communicate results in a clear, results-oriented way. They collaborate with analysts, engineers, and decision-makers to deliver insights that are both technically sound and aligned with company goals. Their impact is measured by how effectively they turn data into decisions.
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Data Scientist Core Responsibilities
- Build statistical and machine learning models to support forecasting, classification, or clustering
- Clean, transform, and validate large volumes of structured and unstructured data
- Develop data experiments and A/B tests to measure product or marketing impact
- Translate complex findings into clear visualizations and recommendations
- Collaborate with product, marketing, operations, and engineering teams
- Monitor model performance and retrain algorithms as needed
- Document methodologies and maintain reproducibility standards
Required Skills and Qualifications
Hard skills
- Proficiency in Python or R for statistical computing and machine learning
- Strong SQL skills for data extraction and transformation
- Experience with modeling libraries (e.g., scikit-learn, XGBoost, TensorFlow)
- Knowledge of statistics, hypothesis testing, and data visualization tools
- Familiarity with cloud-based data tools (e.g., BigQuery, Snowflake, AWS SageMaker)
Soft skills
- Analytical mindset with business orientation
- Strong communication of technical ideas to non-technical audiences
- Curiosity, creativity, and structured problem-solving
- Project management and ability to balance multiple priorities
Education
- Bachelor’s degree in statistics, computer science, mathematics, or a related field
- Master’s degree or PhD preferred for advanced modeling or research-heavy roles
Certifications
None required, but recommended certifications include Microsoft Certified: Azure Data Scientist Associate, IBM Data Science Professional Certificate, and TensorFlow Developer Certificate.
Preferred Qualifications
- Experience working with unstructured data (e.g., NLP, image, or audio analysis)
- Background in experimentation, AB testing, or causal inference
- Familiarity with ML deployment practices or MLOps tools
- Domain expertise in industries such as finance, healthcare, or e-commerce
National Average Salary
Data scientist salaries vary by experience, industry, organization size, and geography. Click below to explore salaries by local market.
The average national salary for a Data Scientist is:
$120,062
Sample Job Description Templates for Data Scientists
Junior Data Scientist
Position Overview
A junior data scientist supports analytics and modeling initiatives by preparing datasets, performing exploratory analysis, and contributing to model development. This entry-level role focuses on learning tools, building technical foundations, and collaborating with more senior team members on business-driven projects.
Responsibilities
- Clean and preprocess structured and unstructured data for modeling
- Conduct exploratory data analysis and create summary visualizations
- Support model development, testing, and documentation
- Assist with A/B testing setup and results interpretation
- Collaborate with analysts, engineers, and other scientists to understand project goals
Requirements
Hard skills
- Proficiency in Python or R for data manipulation
- Strong SQL skills for querying relational databases
- Familiarity with machine learning libraries (e.g., pandas, scikit-learn)
Soft skills
- Eagerness to learn and receive feedback
- Strong attention to detail
- Ability to manage time across multiple tasks
Education
- Bachelor’s degree in data science, statistics, or a related field
Certifications
- None required; Google Data Analytics or IBM Data Science Certificate recommended
Preferred Qualifications
- Internship or academic project experience in data science
- Exposure to data visualization platforms (e.g., Tableau, Power BI)
Senior Data Scientist
Position Overview
A senior data scientist leads the design and deployment of advanced analytics solutions, acting as a technical expert and mentor within the team. They work on high-impact projects and help shape data science best practices across the organization.
Responsibilities
- Lead development of scalable machine learning models
- Guide experimentation frameworks and statistical methodologies
- Translate complex insights into executive-level recommendations
- Mentor junior team members on modeling, code quality, and communication
- Collaborate with engineering on model deployment and data architecture
- Contribute to process improvement and reusable pipeline development
Requirements
Hard skills
- Expertise in predictive modeling, feature engineering, and statistical testing
- Advanced knowledge of Python, SQL, and cloud ML tools
- Familiarity with distributed computing frameworks (e.g., Spark, Dask)
Soft skills
- Leadership in cross-functional settings
- Strong documentation and decision-justification skills
- Proactive stakeholder communication
Education
- Bachelor’s or master’s degree in a quantitative field
Certifications
- Cloud ML certifications strongly preferred
Preferred Qualifications
- Experience supporting product, operations, or customer-focused use cases
- Background in model interpretability or fairness
Lead Data Scientist
Position Overview
A lead data scientist sets the technical direction for a team or functional domain. They drive model strategy, partner with leadership on roadmap development, and standardize methodologies across the organization.
Responsibilities
- Define modeling approaches and feature strategies for high-value use cases
- Lead technical planning for analytics and ML pipelines
- Set and maintain best practices for experimentation, version control, and reproducibility
- Coach team members and lead code reviews for quality and consistency
- Partner with product, engineering, and executive teams to ensure alignment
Requirements
Hard skills
- Deep understanding of statistical modeling and advanced ML techniques
- Experience building ML systems at production scale
- Strong software engineering practices and MLOps knowledge
Soft skills
- Technical mentorship and team development
- Leadership in ambiguous problem spaces
- Clear communication of model assumptions and tradeoffs
Education
- Bachelor’s or master’s degree in computer science, statistics, or related field
Certifications
- None required; advanced cloud or ML engineering certifications recommended
Preferred Qualifications
- Experience in setting organizational modeling standards
- Familiarity with regulatory and ethical considerations in data science
Principal Data Scientist
Position Overview
A principal data scientist is a high-level expert responsible for driving enterprise-wide analytics strategy and leading the development of cutting-edge models. They provide vision for data science initiatives and influence strategic decisions across the business.
Responsibilities
- Architect enterprise-level modeling frameworks and advanced analytics systems
- Identify and prioritize data science opportunities with senior leadership
- Mentor leads and senior scientists across multiple teams
- Promote innovation in ML/AI techniques, tools, and workflows
- Represent the data science function in executive and cross-departmental planning
- Ensure scalability, transparency, and ethical use of data science outputs
Requirements
Hard skills
- Mastery of machine learning, deep learning, and statistical modeling
- Proven experience operationalizing models across distributed systems
- Expert fluency in Python, ML libraries, and cloud-native tooling
Soft skills
- Visionary leadership with strong business influence
- Executive-level communication and negotiation
- Ability to drive alignment across technical and non-technical stakeholders
Education
- Master’s or PhD in a quantitative or technical field
Certifications
- None required; widely recognized technical credentials are a plus
Preferred Qualifications
- Industry recognition or contributions to the data science community
- Experience building data science organizations or platforms at scale