What companies are looking for data scientist?

Data scientists are in high demand these days, as companies across all industries look to leverage data to gain valuable insights and drive business decisions. But what exactly are companies looking for when hiring data scientists? What skills, experience, and attributes make a strong data science candidate?

The Role of the Data Scientist

First, it’s important to understand the role and responsibilities of a data scientist. At a high level, data scientists utilize statistical and computational skills to analyze large, complex datasets, develop models, and translate findings into actionable insights for business objectives. More specifically, common data scientist responsibilities include:

• Collecting, cleaning and organizing data from various sources
• Performing exploratory data analysis to uncover patterns, trends and relationships
• Building and optimizing machine learning models and statistical models
• Developing algorithms and predictive data analytics
• Presenting data findings to key stakeholders using data visualizations
• Translating analytical insights into recommended actions to meet core business goals

Data scientists collaborate closely with various teams – like engineering, product, and business development – to help make data-driven decisions. Their models and analysis inform everything from new product features to marketing campaigns, pricing optimization, risk assessment, and more.

Technical Skills

When evaluating data science candidates, companies look for a solid foundation in technical skills related to working with data.

This includes skills like:

• Programming: Proficiency in languages like Python, R, SQL, Java. Python and R in particular are heavily used for data tasks.
• Statistical analysis: Understanding statistics fundamentals like distributions, statistical testing, regression, etc. Advanced skills in multivariate analysis, time series analysis, A/B testing are valued.
• Machine learning: Knowledge of ML algorithms like regression, classification trees, clustering, neural networks. Plus experience building, evaluating, and optimizing models.
• Data wrangling: The ability to collect, clean, transform, merge, and restructure complex data from diverse sources.
• Data visualization: Designing insightful graphs, charts, infographics using tools like Tableau, Power BI, etc. To communicate data insights.
• Big data platforms: Experience with big data tech like Hadoop, Spark, Kafka for storage and distributed processing.
• Cloud tools: Familiarity with cloud-based data platforms like AWS, Azure, GCP for storing, processing, and serving data at scale.

The strongest data science candidates have a mixture of skills that allow them to derive value from data across the full analytics pipeline. From ingesting raw data all the way to building models and communicating findings.

Education Background

In terms of education, most companies require at minimum a bachelor’s degree in a quantitative field for data scientists. Common fields of study include:

• Computer Science
• Statistics
• Mathematics
• Analytics
• Information Systems
• Data Science
• Physics
• Economics
• Engineering (Software, Mechanical, etc.)

An advanced education like a master’s degree or PhD is also valued by many companies. As data science is an interdisciplinary field, candidates with education mixing technical and business domains stand out.

Industry Experience

Hands-on experience applying data science and machine learning techniques in a business context is a huge plus for companies hiring data scientists.

Relevant industry experience might include:

• Developing predictive models for tasks like customer churn, lifetime value, targeting, demand forecasting, etc.
• Designing and building NLP algorithms for chatbots, search, text analytics.
• Creating computer vision systems for image recognition, object detection.
• Optimizing recommendation engines for content, products, etc.
• Producing analytics reports, dashboards, and visualizations to highlight trends.
• Performing multivariate testing for UX improvements on websites and apps.

While some companies hire data scientists fresh out of college, experience deploying models and analytics in real business environments is highly valued.

Domain Expertise

Expertise in the industry or business domains relevant to the companies hiring is also key. For example, finance companies want data scientists who understand time series modeling and risk analytics. E-commerce companies need NLP and personalization skills. Manufacturing firms value IoT analytics experience.

Having domain knowledge allows data scientists to:

• Quickly understand business challenges and objectives.
• Identify the most relevant datasets, features, and methodologies.
• Build models and analytics tailored to the industry.
• Produce actionable insights vs. generic observations.

Data science candidates with knowledge of the company’s industry and products have a competitive edge.

Communication and Translation

While technical aptitude is critical, companies also value ​​”soft” skills like communication, collaboration, and critical thinking in data scientists. In particular, the ability to translate complex findings into clear, business-focused insights is highly sought after.

Key communication skills include:

• Distilling data analytics into concise executive summaries, reports, and presentations.
• Producing compelling data visualizations and dashboards.
• Conveying technical concepts and model predictions to non-technical colleagues using plain language.
• Developing action plans and strategic recommendations based on data insights.
• Driving data literacy and adoption of data products across the organization.

Data science is a cross-functional role requiring close collaboration with product managers, engineers, analysts, and business leaders. Strong communication skills allow data scientists to relay findings effectively.

Creativity and Business Acumen

Data science is fundamentally a creative process, requiring curiosity, critical thinking, and problem-solving skills to uncover non-obvious insights. Companies look for data scientists who:

• Ask strategic questions to define the right data problems worth pursuing aligned to business goals.
• Rapidly explore data from new angles.
• Think outside the box for innovative modeling techniques.
• Combine diverse data in unique ways to generate insights.

Strong business acumen is also key. Data scientists must stay aligned to business objectives and translate analytics into tangible value – like increased revenue, lower costs, improved efficiency, and reduced risk. An analytical and business mindset allows data scientists to focus their efforts on the highest-impact projects.

Leadership Potential

For more senior data scientist roles, companies look for leadership, mentorship, and project management abilities. Responsibilities may include:

• Leading a team of more junior data scientists.
• Developing standards, best practices, and governance for data analytics.
• Driving adoption of data science results across the organization.
• Coaching analysts and building data/analytics skillsets.
• Scoping, planning, and executing complex analytical initiatives.
• Representing data science at the executive level.

Solid data science skills must be coupled with the ability to lead projects, build teams, and influence executives. Companies want well-rounded senior data scientists with both technical expertise and leadership presence.

Tools and Platforms

Here are some of the most common data science tools and platforms that companies look for experience with:

While not every company uses the same tool stack, these are among the most popular data science platforms. Experience with the relevant tools used at the company is valued highly.

Certificates and Continued Learning

Finally, companies look for a commitment to continuous learning and professional development in data science candidates. This may include:

• Relevant certificates from programs like Coursera, Udacity, edX, etc.
• Participation in Kaggle competitions, analytics challenges/hackathons.
• Attendance at data conferences and meetups.
• Contributions to open source data projects.
• Activity writing blogs, tutorials, books related to data science.
• Maintaining an up-to-date awareness of emerging data technologies and techniques.

Data science evolves rapidly. Companies want self-motivated candidates who actively build their skills and remain on top of industry trends.

Conclusion

Data scientists must wear many hats. They need a solid foundation in technical areas like statistics, programming, and machine learning. Hands-on experience applying analytics to real business problems provides a competitive edge. Strong communication skills are critical for relaying complex insights. Creative thinking and business acumen drive high-impact work. And passion for continuous learning keeps data scientists up-to-date and expanding their toolkit.

Companies seek candidates with a balanced blend of technical depth, business savvy, communication ability, and leadership potential to deliver tangible value from data analytics.