INDUSTRY STATS
According to the U.S. Bureau of Labor Statistics, Statistical Data Analyst positions are at a 8% growth rate, which is as fast as average. With that said, there are currently 168,000 jobs in the market right now. The total number of jobs is expected to increase by 13,200 to 181,200 in the period of 2020-30.
What’s more, the median annual wage for the Statistical Data Analyst jobs was $98,860 in May 2020. The lowest 10% earned less than $54,070, and the highest 10% more than $155,660.
Our conclusion? The Statistical Data Analyst job market is wide open for candidates.
Top Statistical Data Analyst sections that make the best resume
- Header
- Professional summary
- Experience (with numbers and results)
- Relevant skills
- Education
- Certifications
How to write a Statistical Data Analyst resume experience section
Statistical Data Analyst Resume’s Job Experience Checklist:
- Use 4-6 bullet points per job title;
- Don’t go further than a decade behind when describing your job history, unless you’re applying for an executive position;
- Combine job responsibilities as well as achievements with numbers in results when you describe your past work;
- Start each sentence with a power verb and avoid overused buzzwords;
- Use either C-A-R or S-T-A-R methodology, when describing your experience.
The work experience samples below come from real Statistical Data Analyst resumes that got people hired at top companies. You can use them as an inspiration to build your own resume:
- lead a group of 5 people
- Coordinated a team of 20 data scientists working on 6 different projects
- Have Deeper knowledge and understanding of data structure , data mining and analytical tools
- Experience with scripting and development skills in Python/Pearl with deep comprehension of regular expressions
- Knowledge of general networking and security knowledge in areas such as Firewalls, TCP/UDP, Routing/Switching, DNS, NAT, Packet Tracing and Analysis
- Managing 15 customers accounts within Electronics/Toys vertical
- Responsible for the lifecycle of customer experience from activation to support
- Managing external vendors and delivery of data
- Leading a team of 3 data analysts
- Resource planning to ensure on-time delivery of projects
- Proactively identifying and implementing data improvements (systems, technology, use of data)
- Creating and maintaining strong relationship with customers and customer teams
- Translating analysis into literate, concise and logical documentation
- Quality assurance of data harvested from online stores
- Data transformation and loading to custom Data Quality engine
- Development and maintenance of data integrity tests in Ruby
- Development and maintenance of data quality tests in MySQL
- Direct interaction with functions of the business (Sales, Product Management, Engineering) and with customers regarding queries and change requests
- Customer support via Zendesk
- System bug reporting in Jira
- Worked with structured Data for Actionaid and PMI international to perform RFM analysis in python in order to discover seasonality in donations for Actionaid and patterns in customer behavior for PMI.
- Implemented customer churn analysis for a big airline and modelled with 86% accuracy score customer churn, using a random forest algorithm
- Worked closely with a team of data engineers and data scientists to fix bugs and run queries for analysts in SQL.
- Worked under an agile methodology environment and used AWS, Salesforce and Jira software on a daily basis.
- Managed end to end dashboards for various business functions
- Created new process flows across verticals
- Built dashboards and visualization for data coming from the said dashboards for the organization in the CEO's office team
- Created Lead Funnel Management System and setup workflow to keep track of leads and process of the leads, Gathered relevant data about the lead and converted as insights
- Created Web Scraping tool using R Studio to collect the data from the real estate websites and average rental prices across India
- Created performance scorecard for city heads and growth managers to keep track of their performance every month
- Created automated emails using G-Script to across the systems to notify the updates via emails
- Created a recruitment dashboard to track the candidate status, availability, scheduling of an interview and its updates, follow-ups on candidates using automated emails
- Successfully interpreted data to draw conclusions for managerial action and strategy
- Used statistical techniques for hypothesis testing to validate data and interpretations
- Presented findings and data to team to improve strategies and operations Proposed solutions to improve system efficiencies and reduce total expenses
- Used advanced Microsoft Excel to create pivot tables, used VLOOKUP, and other Excel functions
- Designed ad hoc queries with SQL using GoogleSQL/DREAMSQL
- Worked closely with the company to identify customer needs and demands.
- Gathered information from various sources, interpreted patterns and trends using tools such as SQL, Excel and Power BI.
- Presented quality insights and reporting to decision making using tools like PowerPoint, Excel and Power BI.
- Identified opportunities to improve business process and design campaign to increase revenue.
- Responsible for the management of 24 Local Chapters of AIESEC in India
- Key insights from the previous years and giving strategies for the same
- Analyzing the supply and demand for growth in operations.
- Worked on an Inventory Management project and created Power BI reports for analyzing the excess and obsolete stock
- Analysed and developed Hive queries for processing on a 10-node Hadoop cluster with Cloudera distribution
- Reduced the overall handling cost of excess and obsolete stock by 10%
- Designed database schema containing over 30 tables such as user, groups, threads and posts
- Migrated 99% of record mapping from legacy system to Discuz forum software through SQL Server
- Established departmental procedures and workflow that increased productivity by 15%
- Created data visualizations in Tableau that contributed to increasing in funding by $1 million
- Managing data-driven projects addressing diverse business objectives and goals and providing insightful and actionable analytics
- Focus on Automation, Continuous Audit, and Risk Assessment
- Utilizing SAP, TeamMate, and third-party data for holistic and accurate Internal Audit process and outcome goals progress reporting
- Designing automation data workflows using ACL, Knime, and Tableau Prep
- Developing R scripts and processes for automation testing and statistical analysis as well as R Markdowns for documentation purposes and reproducibility
- Advanced analytics and BI techniques for trend analysis, anomaly detection, and automated alerts based on business benchmarks and KPIs
- Presentation and storytelling with Tableau dashboards
- Improved the precision of an existing regression model used to estimate probability of getting a job from 96.2 percent to 99.3 percent
- Performed continuous maintenance on MS Access and SQL databases and generated dashboards on Tableau
- Reduced report generation time by 75 percent using automated surveys and eliminated manual calculations using VBA macros
- Preparation and Delivery of DPIC reports. Highlights include : Demand Planning Intelligence Consortium Data wrangling Process Execution and Verification Result Preparation Client Intimation and Communication Process Alterations and Modifications Process transition from SQL to Alteryx Ad-Hoc Analytical Reports
- SFaaS (Statistical Forecasting as a Service). Highlights include : Studying the needs of new clients and modifying Alteryx workflows according to their requirements. Research and Development work for process improvement, primarily focusing on R programming. Preparing Tableau Dashboards for existing reports in Power BI. Report Verification and testing.
- Ad-Hoc Works under Consulting Banner. Highlights include : Provide Outside Analytical help when required Data wrangling and Data interpretation to derive meaningful insights Preparing Tableau Dashboards for actionable outputs Report verification
- Integration and explotation of online and offline customer data
- Digital & Offline client behaviour models
- Customer Segmentation through models
- Analyzing complex data from multiple sources, detecting any problems with the performance of our campaigns and suggesting improvements that will have immediate impact
- Analysis and definition of KPIs to identify business opportunities and achieve sales goals
- Definition and creation of a reporting system that will present actionable insights to teams across the business, using tools like tableau and alteryx
- Consult with clients and internal stakeholders to gather requirements and plan milestones in the research, development, and implementation phases of the project life-cycle
- Develop Tableau reports that provide clear visualizations of various industry specific KPIs
- Access and transform massive data-sets through filtering, grouping, aggregation, and statistical calculation
- Strong understanding of advanced Tableau features including calculated fields, parameters, table calculations, row-level security, joins, data blending, and dashboard actions
- Created Drill-down and Sub-Reports. Monitored the performance of reports
- Generated Dashboards with Quick filters, Parameters and sets to handle views more efficiently
- Generated context filters and data source filters while handling huge volume of data
- Built dashboards for measures with forecast, trend line and reference lines
- Published Workbooks by creating user filters so that only appropriate teams can view
- Web scraping tool algorithmscraping information of credit cards from website
- Neo4j Graph Database Recommender System
- Differentiate between Graph-based RecSys and Machine learning based RecSys
- Cypher queries to create a data set, relationships in neo4j
- Exploratory Data Analysis
- Creating deep feature synthesis and data analysis using pandas Validation, analysis & Testing of Dashboard
- Cleaning data, Restructuring the data and writing a script to categorize the data into Neo4j format
- Lead and oversee full-stack Business Intelligence solutions that leads to direct business impact
- Work cross-functionally with Marketing, Product, Finance teams and translate business requirements into SQL queries to answer business questions
- Build 70+ complex Tableau dashboards with advanced functions to provide insightful analysis, identify growth opportunities, and maximize profits
- Create custom Tableau Report Portal with Microsoft SQL to show near real-time usage and provide easier navigation
- Extensive data cleansing and analysis in MS Excel, using pivot tables, formulas (v-lookup and others), data validation, conditional formatting, and graph and chart manipulation
- Create reports and metrics, present them convincingly, facilitate required discussion
- Using Excel pivot tables to manipulate large amounts of data in order to perform data analysis, position involved extensive routine operational reporting, hoc reporting, and data manipulation to produce routine metrics and dashboards for management.
- Responsible for the development and preparation of a broad range of reports and complex analysis focused on program performance and project deliverables. Created PowerPoint decks to produce clear concise and professional presentations quickly for management and other analysts. Provide timely status reports and metrics.
- Service Transfer projects SME (workflow mapping, operational platforms design, creation of manuals and job aids)
- Coordination of Pricing Points setup for Western Europe (SAP, Vendavo)
- Master Data cleanse and integrity maintenance (profit centre and unit of measure)
- Scoring ESG: participation à la refonte du SI de scoring
- Reporting et analyse: conception et mise en oeuvre d'indicateurs d'analyse des scores, outils de data visualisation pour les gérants (Power BI), génération automatique de convictions ESG (Yseop)
- Soutien des analystes ESG: support, participation aux revues sectorielles
PRO TIP
Don't make the same mistake everyone else does. What we mean is, don't list your Statistical Data Analyst job responsibilities instead of your achievements. Recruiters know what you do. They want to know what kind of difference you can bring to their company. Focus on what you've accomplished.
Action Verbs for your Statistical Data Analyst Resume
Recommended reads:
Statistical Data Analyst Resume Skills’ Tips & Tricks to Impress Recruiters
Resume Skills Section Checklist:
- Ensure your hard skills section (including technologies) are exactly matching the job description.
- Don’t simply list your soft skills. Apply the “show, don’t tell” principle - let your job achievements speak for themselves.
- Find a way to showcase your skills beyond the skills section.
- Your resume’s skill section is important to ATS systems - so don’t skip it.
Top Skills for your Statistical Data Analyst resume
- Python
- SQL
- R
- Machine Learning
- Java
- Tableau
- Communication
- Curiosity
- Business mindset
- Adaptability
- Critical and analysitcal thinking
- Problem solving
PRO TIP
When describing your experience, don’t go too far from its terminology. Recruiters use ATS systems to filter resumes based on them having certain keywords, so make sure you use at least a few keywords mentioned in the job description.
Recommended reads:
Statistical Data Analyst Resume Header: Tips, Red Flags, and Best Practices
CHECKLIST For Your Statistical Data Analyst Resume Header
- Your name and surname in a legible and larger resume font
- The job title you’re applying for or your current job title as a subheading to your name
- Link to your portfolio or online profile, such as LinkedIn
- Address (City and State for the US; just your city for rest of the world)
- Email address
- Headshot (required or welcomed in the EU; not required and sometimes frowned upon in the US)
Stick to popular email providers such as Gmail or Outlook. And use these professional formats to create your username:
- first.last@gmail.com
- last.first@gmail.com
- firstlast@gmail.com
- f.last@gmail.com
- first.l@gmail.com
Recommended reads:
PRO TIP
Some companies, states, and countries have non-discrimination policies about what kind of information can be included on your Statistical Data Analyst resume. This might include a photo (which is often included in a resume header and might be on personal web pages you link to). You can always email the company’s HR department to ask about their policies before you apply.
Statistical Data Analyst Resume Summary Best Practices
Checklist: What to include in your Statistical Data Analyst resume summary:
- Years of experience;
- Highlight top 3 skills and proficiencies;
- One big professional accomplishment you’re most proud of, that you can tie with the aforementioned skills;
- Use short, direct sentences - but no more than three - to keep the HRs interested.
Resume Summary Formula:
PRO TIP
Your summary section should act as a professional taster. Use it wisely. Effectively convey your professional profile and let the hiring manager know that if they hire you, they won’t be disappointed. Make sure to include keywords from the job description too! Elaborate on your abilities further in your experience section. Again, cater to the job description.
Recommended reads:
Listing Your Education, Certifications and Courses
Resume Education Section Checklist:
- Ensure your hard skills section (including technologies) are exactly matching the job description.
- Don’t simply list your soft skills. Apply the “show, don’t tell” principle - let your job achievements speak for themselves.
- Find a way to showcase your skills beyond the skills section.
- Your resume’s skill section is important to ATS systems - so don’t skip it.
Top Certifications for your Statistical Data Analyst resume
Recommended reads:
PRO TIP
There are dozens of certifications that you can claim as a Statistical Data Analyst. But, some are more effective than others. That’s why you mustn’t include every certificate other applicants might have. Try instead to earn and list a few of the difficult ones.
Statistical Data Analyst Resume: Additional Writing & Formatting Tips
There are three basic resume formats you can choose from:
- Reverse-chronological resume format;
- Functional resume format;
- Hybrid (or Combination) resume format;
The most optimal format for your particular case will depend on your years of experience, as well as whether you’re switching industries or not.
Reverse chronological resumes are best suited for experienced individuals who are sticking to their industry. The experience section takes a central place, and its bullets contain your responsibilities and achievements, coupled with numbers and results.
Functional resumes are used by less experienced jobseekers or career changers. Note that it’s not a format that recruiters prefer, as most are used to the classic chronological alignment. Instead of a list of job titles, functional resumes focus on your skills, and through what experiences you gained them.
Hybrid resumes are great for both experienced and entry-level candidates, as well as career changers. They combine the best of both worlds - most often in a double column format, where one side of the content is focused on your experience, whereas the other - on your skills, strengths, and proudest moments.
Statistical Data Analyst Resume Summary best practices
Here are more resume tips regarding your layout and style:
- Clear and legible 12p resume font size;
- Use 10’’ resume margins - that’s default for a great resume design;
- Use a one-page template resume length if you’ve got less than 10 years of experience; otherwise, opt for a two-page resume;
- Save your resume as PDF before sending it to the recruiter.
To take it a step further, check out how your resume can stand out without leaning too much on the creative side.
Recommended reads:
PRO TIP
Test your draft Statistical Data Analyst resume by sending it out to peers and mentors in your circles. Ask them to review it as if they are hiring you for a project and implement the feedback afterwards.
Other sections to include in your resume
Depending on the type of company (corporation or start-up; innovative or traditional), job seniority level and your location, you may want to include more sections to your Statistical Data Analyst resume:
Statistical Data Analyst Resume: How to Make Yours More Creative & Stand Out
When you send your resume to a potential employer, chances are it's the fiftieth one they've seen that day. That's why you need to make your Statistical Data Analyst resume stand out for the right reasons. That means showing your personality, not just your professional experience. Employers are far more likely to remember a candidate who seems like a genuine person and not a robot. Do this by including your passions (which is also a great place to demonstrate skills on a resume), share your favorite books, or even what your usual day looks like.
What Makes a Great Statistical Data Analyst Resume: Key Takeaways
- Choose a resume layout that sends the right message across and fits your current career situation;
- Create a resume header that shows your desired job title, and easy to find contact numbers;
- Be specific about your experience, accomplishments and future goals in your summary;
- Feature detailed metrics and specific examples that show the impact you made in your previous roles when describing your experience;
- List soft skills backed by examples;
- Add all of your technical skills and certifications that you have and match the job description;
- Show off a dash of personality in your resume that will demonstrate your culture fit and the right mix of hard and soft skills.
