Data Scientist Resume Sample

#1 Data Scientist – Research Assistant

• Developed and lead Arduino projects based on C++.
• Lectured on C to approximately 200 students.
• Assisted faculty with in-depth data research in both lab and office environments.
• Used Excel to enter data into the project database and provided updates on a weekly basis.
• Validated incoming data to check information accuracy and integrity while independently locating and correcting concerns.

#2 Data Scientist – Data Analyst

• Analyzed different aspects in Blockchain to incorporate parking availability for the citizens of Detroit.
• Planned and conceived computer systems using information engineering, data modeling, and structured analysis.
• Expedited industry-wide recognition, company awareness, and valuable insights for Detroit Used programming capabilities in PHP, SQL and JavaScript and other libraries as needed.
• Researched new technologies, software packages, and hardware products for use in website projects.
• Applied knowledge of data modeling and statistical analysis to note trends and draw conclusions.

#3 Data Scientist – Research Fellow

Development of a tool to support decision making about the feasibility of breakwaters.
This work goes through 3 phases:

• Phase 1: Acquisition and treatment of meteo-oceanographic data. The development of some routines with Python allowed us to speed up much of the mathematical treatment involved.
• Phase 2: Use of a numerical model of sea waves. I created some algorithms that allow procedure automation based on data creation and management, using Python libraries such as Pandas, Numpy, Scipy, and Matplotlib.
• Phase 3: Neuronal network application.

#4 Data Scientist – Responsibilities

• Providing data analytical solutions to various problems along with business-driven data insights.
• Contributing to designs and launches, innovative and complex analytic models, utilizing a blend of contemporary & traditional data mining techniques, which are applied to both structured & unstructured datasets.
• Managing automation projects for the smooth functioning of various processes and portfolios
• Participating in technology planning & direction, strategy development, leadership and implementation, business and operational transformation execution, business solution delivery, and business development.

#5 Data Scientist – Data Analyst

• To predict high priority tickets using the Random Forest classifier.
• Used predictive analytics such as machine learning and data mining techniques to Forecast the incident volume in different fields, quarterly and annually.
• Coordinated with the clients to know about the business problem.
• Involved in the continuous enhancements and finding the best solution.

#6 Data Scientist – Data Analyst

• Performed Data cleaning, transformation, validation with the purpose of understanding or making conclusions from the data for decision-making purposes.
• Implemented descriptive statistics and visualization techniques to check the data normality.
• Selecting features, building and optimizing classifiers using machine learning techniques
• Worked on the Account receivable data to predict potential Defaulters for the payment and tested the data with different algorithms (SVM, GBM, Random forest) and implemented with Random forest.
• Worked on Services data and analyzed the data between orders inflow with Inventory and created a visualization.
• Improved Model performance with the help of parameters tuning and customizing the tuning parameters.

#7 Data Scientist – Responsibilities

• Organized large datasets to obtain actionable insights including finding innovative ways to combine fields
  of data and ensuring high-quality data management techniques.
• Performed Data cleaning, transformation, validation with the purpose of understanding or making
  conclusions from the data for decision-making purposes.
• Performed model building and data processing using Random Forest, logistic algorithm over a Policy
  issuance cycle time predictions. Used an ETL process to clean the data and feed it into ML.
• For the travel claim request by using the Python and machine learning techniques like Clustering, Logistic identified the Key entity like flight number, seat number, travel date and time, Business class information and share to the corresponding team.
• Worked on the classification use case and tested the model with Logistic, Random forest, XGB algorithm and deployed with the Random forest model based on confusion matrix validation. Calculated accuracy, precision, recall and F score and identified the best model

#8 Data Scientist – Data Analyst

• Completed the requirements in data model tool using GEHC BI COE standards
• Created and Maintaining System Design Specifications (SDS), DDL and indexing scripts.
• Developed data architecture, data modeling by using the Erwin tool, and ETL mapping solutions and data warehouse consistency Assessed current technical architecture and estimated cost for technical components.
• Designed the Entity model, CDS model, and logic in Greenplum.
• Designed Summary, snapshot design based on requirements and responsible for query tuning and
  optimization

#9 Data Scientist – Audit Analyst

• Evaluate reinsurance risk valued at $2.1 trillion for systemic errors due to poor data quality, flawed business logic, and contract compliance.
• Query large data sets in an Oracle environment using complex SQL programming and perform complex analysis in Excel
• Frequent client contact and communication to work through difficult conversations regarding payment discrepancies.

#10 Data Scientist – Data Analyst II

• Outbreak modeling for C-suite:
• Using flu seasons proxies, the present potential financial impact from various infection scenarios on membership
• Opioid Use Disorder predictive model:
• Trained Xgboost model on 2.5M members’ claims history and
  demographic data to risk score for opioid use disorder.
• Create KPI analyses for web portal and call center utilization:
• Project expected decrease in calls due to web portal enhancements
  and subsequent headcount reductions via the Erlang model.
• Support the Microstrategy clinical KPI dashboard:
• Calculate performance goals using standard deviations as the goalposts.
• Collaborate with clinical partners for new metrics and ad hoc deep dives

#11 Data Scientist – Data Analyst

• Project in Python: to predict house prices using time series analysis and neural networks
• Project in R: to identify factors that predict which employees will have the best performance and which will benefit from a change in their job position using machine learning (XGBoost)
• Project in R: Predicting mortality of small cell lung cancer patients and identification of important prognostic
  factors

#12 Data Scientist

• To analyze the health of underserved communities through data monitoring and the creation of visual dashboards.
• Designs, prepares, tests, and debugs data frames using Pandas.
• Develops, validates and executes algorithms and predictive models to understand the health of a population.
• Identify factors that predict the overall health of individuals and their community.
• Designed monthly visual data dashboard that simplified the trends and patterns within the community for the organization’s assets.
• Coordinated with the stakeholders on project progress and presented results to community health workers and the
  community.
• Involved in the continuous enhancements and finding the best solution to the system.
• Created a data monitoring and manual for the organization to document the method of data monitoring.

#13 Data Scientist – Technical skills

• Algorithms: Linear & Logistic Regression, Decision Tree, Random Forest, KNN, GBM, SVM, Naive Bayes,
  Clustering, Forecasting algorithms like ARIMA, Hybrid, HoltWinter, Stlm.
• Languages: R-Programming, Python
• Database: MS-SQL, MongoDB
• Frameworks: Parallel Processing, 5C Model Framework
• Domain: IOT, DTH, Election, FMCG, Retail

#14 Data Scientist – Core Competitions

• Statistical Analysis
• Data Modelling
• Machine Learning
• Data Analytics
• Project Execution
• Project Architect
• Client Reporting

Data Scientist Resume with Writing Guide

In the job market right now, people are scrambling to figure out how to land their next data scientist position. This is a very lucrative career path, but with so much competition, it’s hard to stand out in the crowd. So we’ve created a Data Scientist Resume Writing Guide that you can use as a resource in your search for the perfect resume. We’ve included examples of some popular resumes and cover letters that you may find useful as well!

Data Scientist Resume Writing Guide:

Step 1:Make a list of all of your accomplishments including what you have done at every job or internship that you’ve had. Listing accomplishments is the key to making a great resume.
Step 2:Give yourself credit for where ever possible. Don’t just say “worked at McDonalds” put the accomplishments into action. For example, “Responsible for training and supervising 10 new employees.
Step 3:Include a section for job duties and responsibilities. This is where you list skills you’ve learned. This is the most important section of your resume because this is where companies will learn about your skills and how you can help them.
Step 4:Put your contact information at the bottom of the page so people can get in touch with you. It’s important to make your contact information as easy to find as possible.
Step 5:Make sure that everything is proofread carefully. This step can’t be understated, because resumes that aren’t proofed well don’t perform as well as others.
Step 6:Make sure that you also include a cover letter. In the cover letter be sure to tell the hiring manager why you are applying and what your goals are for the future.
Step 7:Finally, make sure to distribute your resume to as many places as possible. You can start by posting it on job search sites online (we recommend Monster and Indeed) and in many career centers around campus. Don’t be afraid to go in and hand them off.

Finally, remember that you are unique and that what works for others might not work for you. Use this guide as a tool to help build your resume, but don’t feel like you need to copy it verbatim. Remember, the most effective resumes show companies what skills you have and why they matter to the company’s mission. So make sure that yours does too! When you’re done with your resume, be sure to distribute it widely through multiple channels. And, thanks for taking the time to read this article!

Data Scientist Responsibilities:

1. Perform data mining and statistical analysis on large databases using a range of techniques (e.g., clustering and classification, regression analysis, pattern recognition) to extract meaningful information and knowledge.
2. Design algorithms to optimize the performance of a system or to achieve desired objectives (e.g., increase profit or reduce the cost of performing some action).
3. Evaluate and analyze organizational data from a variety of sources in order to provide management with information used for decision making, policy development, etc.
4. Use the results of computer simulation studies to identify and resolve problems.
5. Develop mathematical models of business processes to assist management in decision making and strategic planning, possibly through the use of large commercial software packages such as LISREL, CAST or PLSQL.
6. Study information-processing activities by performing statistical analyses of employees’ tasks and workloads, the effect of computer programs on performance, or other human factors problems related to information processing.
7. Formulate recommendations to management about how to use and improve information technology.
8. Research and develop mathematical models to describe and predict systems behavior.
9. Design surveys or other types of questionnaires, analyze the resulting data, prepare reports on survey findings, and make recommendations based on results of analysis in order to improve performance or to indicate areas for further research.
10. Prepare database designs or prototypes for data retrieval systems, such as online analytical processing (OLAP) systems or online transaction processing (OLTP) systems.

Top 10 Must-have Data Scientist Skills:

1. Analytical Skill
2. Business Knowledge
3. Communication Skills
4. Computer & Software Skills
5. Data Management Skills
6. Data Visualization Skills
7. Domain Expertise
8. Mathematical
9. Modeling & Analysis (e.g., matlab, R)
10. Numerical and Statistical Methods
    5 Programming Languages
    6 Software Tools –
    7 Statistical Analysis (e.g. SAS, R)
    8 Use of Algorithms (e.g., machine learning, econometric, etc.)
    9 Statistics and Research Methodology
    10 Writing and Speaking Skills

Tips to write a Data Scientist Resume Summary:

1. Begin with a summary of your experience. This will be the first thing that will appear on your resume and it will be the first thing to impress an interviewer, so make sure this is good.
2. Make a strong first impression; keep the focus on you!
3. Keep it simple and personal to give a strong kick-start to your Data Scientist Resume Summary.
4. When relevant, add details of your work experience and education.

Tips to write a Data Scientist Resume Objective

1. Begin with an objective that clearly focuses on your role and responsibilities in the organization. This objective will help you target a specific area of interest for someone on the job market seeking to discover Data Scientists. It will also give you a quick snapshot of what the Data Scientist you are targeting can expect when in conversation with you.
2. Always remind the recruiter of your objective in the first paragraph of the Data Scientist Resume Summary.
3. Be clear and concise; be as specific as possible with this section to get you a job interview.

How to write a Data Scientist Resume with No experience:

At the end of this guide, I have presented a sample Resume that demonstrates how to write a resume if you have no experience in the data science field. If you are not familiar with resumes, I recommend reading this article written by Joanna Castro that gives great tips and tricks for writing your own resume. It is important to note that while the sample resume I have provided can be used as a template for your own Data Scientist Resume, it is very important to customize your resume according to your experiences.

Tips:

1. Always include the specific skills you have acquired during your academic or work experience.
2. Always include a City & State/Province for every professional and academic experience you have had.
3. Never use standard abbreviations or acronyms without explaining them to the reader.
4. Always include a list of technical work skills you have acquired throughout your career, along with their level of mastery and description, e.g: “HTML-5, JavaScript, jQuery” or “Ruby on Rails”.

Key Takeaways:

• Use bullet points for sections. This will allow you to add more information and help the reader digest your resume quickly, while keeping it short and to the point.
• Avoid using jargon or acronyms if possible. If you have to use them, add explanatory sentences about their origins in the beginning.
• Format your resume based on your target job’s requirements. Data Scientists and Programmers are both in great demand right now – so your resume should look attractive to both types of employers. Keep your resume clear, organized, and visually appealing.
• Use examples from your experience in your resume. It’s important to have concrete evidence of your skills and abilities that you can show to prospective employers.
• When you’re writing the summary statement, include a paragraph about yourself that helps the reader understand how you’re relevant to the job and why you’d be great in it.
• Do not use “objective” on your resume unless it is specifically requested by the employer.