Top 10 Data Science Skills Recruiters Look for!

In this video, we will cover top skills that recruiters look for in data science applicants. 1. Data Science Portfolio Recruiters look for a solid portfolio of data science projects that demonstrates practical knowledge, skills, and experience. It is recommended to leverage Kaggle & Github to build powerful diverse portfolio of projects that showcase your skills in data wrangling, feature engineering, exploratory data analysis, machine learning, and deep learning. 2. Programming/Coding Skills Recruiters look for strong coding background in Python or R programming languages. Several companies require candidates to pass a coding interview that entails data structures and algorithms. 3. Data Wrangling skills Data Scientists need to possess skills in data cleaning, feature engineering, database management (SQL) and big data. Data Scientists can get started by learning Pandas library which is a powerful open-source library in Python used to perform data wrangling. 4. Statistics & Math Fundamentals Recruiters and hiring managers look for a solid understanding of mathematics, statistic, probability, and optimization. These skills are critical to enable data scientist gain useful information from the data and empower companies to make better informed decisions. 5. Machine Learning Skills & Familiarity with ML Frameworks Data scientists need to possess knowledge of machine learning to build models that solve both regression and classification-type problems. Scikit-Learn and Keras libraries are easy to learn and can be used to train and evaluate several ML models. Familiarity with several ML Frameworks such as Tensorflow, PyTorch is a plus. 6. Work Experience Having practical work experience in the field of data science is a key requirement that employers seek in job applicants. Top candidates have exposure to key data science project lifecycle steps such as data collection, data wrangling, exploratory data analysis, model training, testing, deployment, and monitoring. Experience in building end-to-end AI/ML workflows that can address scalability, security, latency, and maintainability is a definite plus. 7. Data Visualization & Story Telling skills Data scientists need to possess the ability to tell a story from data using visualization and dashboards. Matplotlib, Seaborn, PowerBI and Tableau are great tools to visualize data and gain valuable insights. 8. Solid Communication Skills Recruiters look for solid communications skills in potential candidates. Data Scientists need to think critically, gain valuable insights from the data, and communicate them clearly to leadership in an effective manner. 9. Certifications Having Data Science or Machine learning certifications could help candidates land an interview and potentially get hired. Example certificates include but not limited to Microsoft Certified: Azure Data Scientist, Amazon Web Services (AWS) Machine Learning, Google Cloud ML Certification, and TensorFlow certification. 10. Problem Solving & Good Reasoning Skills The role of data scientists is to solve problems using data. Data Scientists need to possess problem-solving skills, good reasoning and thought processing skills. Data Scientists need to ask the right questions, determine why an issue is occurring, propose creative solutions, implement those solutions, and assess their effectiveness. I hope you enjoyed this video and found it useful and informative! #datasciencejobs #datascienceinterview