Data Scientist job description template
An effective description can help you hire the best fit for your job. Check out our tips to provide details that skilled professionals are looking for.
Tips for Writing a Data Scientist Job Description
A data scientist is a professional who uses statistical and analytical techniques to extract meaningful insights from raw data. They can leverage that knowledge to build models (including machine learning models) and predict trends. An experienced data scientist can also clean your data sets to accelerate your time-to-insight of your big data analytics pipelines. This is important to businesses since the amount of available data is growing exponentially.
Below is a sample job description exploring the daily responsibilities and necessary qualifications for a data scientist.
The Job Overview
We are seeking a data scientist to join our business intelligence team to help us make better business decisions based on our data. The ideal candidate will have a working knowledge of statistics, mathematics, and data science programming languages (e.g., SQL, R, Python).
Your primary responsibilities will be performing statistical analyses, running custom SQL queries, and identifying patterns and trends that can improve our products’ and services’ efficiency and usability. You will also be expected to maintain and improve our data infrastructure and tools.
Responsibilities
Below are some of the responsibilities a data scientist is expected to assume in their position:
- Work with stakeholders to identify key metrics and opportunities for improving business processes, products, and services
- Build, deploy, and maintain data management systems and back-end data infrastructure for our business intelligence pipeline
- Perform data mining, exploration, and analysis
- Create data visualizations, reports, dashboards, and data audits
- Design, train, and implement machine learning algorithms
- Leverage predictive models to optimize customer experiences
- Creating automated anomaly detection
Job Qualifications and Skill Sets
Below are the qualifications expected of a data scientist:
- Bachelor's degree in data science, data analytics, or related field
- Master’s degree or Ph.D. in a quantitative field, such as statistics, computer science, mathematics, or engineering
- Proficiency in Python, Java, or Kotlin
- Knowledge of data science toolkits such as R, NumPy, and MatLab
- Experience with big data analytics technologies such as Spark and Hadoop
- Experience with data visualization tools such as D3.js and Tableau
- Expertise in data mining and machine learning
- Working knowledge of statistical models and business intelligence
- Familiarity with cloud-based infrastructure
- Ability to store and process unstructured data with NoSQL databases and machine learning models
Data Scientists you can meet on Upwork
Andreas A.
Data Scientist
Data Science
- Microsoft Excel
- Data Extraction
- Artificial Intelligence
- Data Visualization
- Data Analysis
- Neural Network
- TensorFlow
- Python
- Machine Learning
- R
- RStudio
- Deep Learning
- Natural Language Processing
- Mathematics
A talented problem solver with a passion for analytics and programming, diligence and a keen eye for detail attribute to my inquisitive nature. Excellent analytical skills with a rigorous Mathematical background-achieving a first-class honours degree in Mathematics. Master degree was on Msc Data Analytics managing to achieve a distinction classification which was on the top results compared with other candidates. Expertise includes: • Proficiency in Python, Machine Learning, R, Artificial Intelligence, and Mathematics. • Strong background in mathematical concepts and statistical analysis. Some of the projects undertaken: • Modeling the price of bitcoin using Python • Sentiment analysis on tweets using Python • Modeling the quality of different wines using R • Machine learning models for predicting the different types of cells in images using Python • Construction of optimal pipelines for supervised algorithms using Python
...Discha Ari Kusuma D.
Data Scientist
Data Science
- Artificial Intelligence
- MATLAB
- Python
- JavaScript
- WordPress
- PHP
- Laravel
- jQuery
- Vue.js
Hi, I'm a Profesional Machine Learning Engineer and Fullstack Developer. My career started in 2015. My competency is in Machine Learning, Matlab, Python, PHP, Wordpress, Laravel, and VueJS Matlab and Python are my experts with more than 5 years experience. Both of them I always use for various data processing such as cellular provider customer service, ecommerce service, and other service. I have analyzed various system such as mountain mitigation systems, ocean wave systems with various methods such as Neural Network (NN) and Fuzzy Logic. Beside these methods, I'm ready to learn new analyst method, where it makes my work better, faster and more efficient. For Fullstack Developer, I have experience building and maintaining website PHP like WordPress, Laravel, CodeIgniter. Also more expert with VueJS, and ReactJS. Skills Data Scientist 👉 Machine Learning 👉 Matlab 👉 Python Skills Fullstack Developer 👉 Website Optimize: CoreWebVitals 👉 FrontEnd FrameWorks : VueJS, ReactJS, NextJS 👉 PHP Framework : Laravel, CodeIgniter, Symfony 👉 CSS Framework : Bootsrap, Tailwind 👉 CMS : Wordpress 👉 Front End Development 👉 Javascript, jQuery 👉 Unit test (Jest) Thank you for your attention.
...Chunyi W.
Data Scientist
Data Science
- SAS
- R
- Linear Regression
- Data Visualization
- Quantitative Analysis
- Statistics
- Analytics
- Logistic Regression
- Big Data
- Biostatistics
- Statistical Analysis
- Epidemiology
- Healthcare & Medical
- Public Health
I obtained my Ph.D. degree in Epidemiology at the University of Michigan and I also have the SAS Programmer certification. Currently, I am a Lead Data Analyst in Medical School. I have a strong background in biostatistics/ epidemiology and have 14 years experiences on analyzing large epidemiological, clinical, genetic and National Inpatient Sample data using various software packages (SAS, SPSS, R and R studio program). I have extensive knowledge of statistical models, and have developed various analysis strategies for different studies and meta-analysis. Statistical methods that I have applied in the research projects: 1. Multilevel Logistic Regression Models, and Ordinal Logistic/Logistic Regression Models 2. Linear Mixed Models and Linear Regression Models 3. Survival Models, Cox Proportional Hazards model, Accelerated Failure Time Modeling, Kaplan-Meier Plot) 4. Poisson Regression Model 5. GEE (Generalized Estimating Equations) 6. Propensity Score Matching (PSM) 7. ROC curve, ANOVA, T-test, Nonparametric Statistics (Kruskal-Wallis test and Wilcoxon Signed Rank Test), Cohen's alpha, Pearson's Correlation Coefficients, Chi-squared test. 8. CMS-HCC Risk Adjustment Model (HCC, RxHCC, ESRD) 9. Data analysis with weighted data in the survey sample. 10. Power Analysis In addition, I have performed the statistical analysis by using the large longitudinal national data in the past: A. Health Retirement Study B. National Health and Nutrition Examination Survey (NHANES) C. National Inpatient Sample (NIS), and Healthcare Cost and Utilization Project (HCUP)) D. CMS-HCC Risk Adjustment Model (HCC, RxHCC, ESRD) E. Meta-analysis to perform the analysis on a large database (Genome-Wide Association Studies) efficiently. . As a data scientist, I am passionate about data analysis, solving complex and interesting task. Once you hire me as a freelancer, the results will be delivered to you within 1-10 days (including weekends). Small project: 1-4 hours. Results will be delivered within 1-2 days. Medium project: 4-10 hours. Results will be delivered within 2-4 days. Large project: 10-20 hours. Results will be delivered within 4-6 days. Project more than 20 hours: Results will be delivered within 5-15 days. Please feel free to contact me and I will response your message within 24 hours. Thank you.
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