Here’s a sample resume we recently produced for a Data Scientist in Australia.
In this article, you’ll learn:
- About the Data Scientist Role
- What Hiring Managers Look For In Data Scientist Resumes
- Data Scientist Job Opportunities
- Job Hunting Tips from our Resident Headhunter
Data Scientist CV [Full Sample]
Here’s a full Data Scientist resume sample, which we wrote for a client recently.
- Highly experienced Senior Data Science Professional with >6 years’ experience in predictive modelling and statistical analysis of vast data sets to solve business questions for the Healthcare, Telecommunications, Finance and Education industries.
- Strong analytical skills with hands-on experience in building predictive models and using advanced machine learning tools to derive actionable insights from consumer data, having implemented LSTM for revenue projection of ABC Bank POS terminals and developing strategy to realise MYR1B growth in merchant deposits.
- Team-player with expertise managing cross-functional technical and sales teams to lead business development initiatives by using consumer data to identify product-specific customer bases and market entry points.
- Boosted customer retention at ABC Bank by 25% and Premier status customer eligibility by 28% through the use of Density Based Spatial Clustering and RFM analysis to recommend customer segmentation and improve marketing strategies.
- Achieved 70% revenue growth in FY 2018 for CDE Tech. by sourcing new suppliers, expanding product variety and expanding market reach from Australia to Singapore and Indonesia.
[This section gives a good helicopter view of the candidate. Quantifying the current role where possible also helps to give scale to the current responsibilities.]
- Drive consumer analytics capabilities of consumer banking, business and investment banking divisions; articulate insights from consumer databases to assist the segment managers, portfolio managers and executive team in decision making. [Opening line succinctly sums up the candidate’s current role as it speaks about primary responsibilities and scope.]
- Conceptualize and build predictive analytics models for analyzing consumer spending data by integrating advanced machine learning tools with big data analytics techniques.
- Interface closely with non-technical teams to identify potential areas for improvement and communicate statistical results in an easy-to-understand and highly business relevant manner. [Show that you’re capable of communicating effectively with diverse organisational stakeholders.]
- Attained 8x increase in customer acquisition by using Random Forest classification to apply look-alike and propensity modelling for studying consumer demographics, credit card usage and spending behavior of existing customers with unit trust accounts. [Talk about your contributions (and the approaches you took on to achieve them) to the company and how it improved operations or drove business results]
- Improved existing predictive model by 3.2x by implementing Gradient Boosting Classifier to enhance targeting accuracy of prospective Premier clients. [Cite quantifiable metrics to support your achievements. Do, however, be aware of your company confidentiality clause.]
- Spearheaded 29% increase in customer acquisition for a prestigious account by identifying clients who fail to meet thresholds and cross-selling alternative financial products. [Cite quantifiable metrics to support your achievements. Do, however, be aware of your company confidentiality clause.]
- Directed team of 9 to maintain product pipeline and maximize profit margins by highlighting high profitability products and presenting strategic market penetration plans to manufacturers for various products. [Simple, clear overview of the job you previously done.]
- Spearheaded internal digital transformation to kickstart data-driven decision making by the Sales and Management teams; implemented comprehensive SSOT database to standardize business data and boost Sales and Management teams’ ability to utilize sales statistics for decision making.
- Analyzed historical data of tendered projects and product pipelines, and collaborated extensively with government agencies and engineering consultants to determine product segmentation, areas for improvement and potential markets to penetrate.
- Built cross-functional sales team of 6 engineers and project team of 8 engineers; trained team to understand market landscape of the Wastewater Industry, assisted team in client meet-ups and regularly monitored team performance in accordance with corporate KPIs.
- Improved delivery time by 23% and attained 180% order book expansion by implementing customized system for tracking sales quotas, reducing redundancy in reporting work and enhancing CRM capabilities.
- Executed 4 complex projects relating to data governance and statistical data analysis for individual clients and organizations including the 123 Co., QWE Inc., and MNOP Org.
- Oversaw full lifecycle of projects, from collaborating with clients for data acquisition and cleaning to building predictive models and presenting relevant results from statistical experiments.
- Australia Cancer Center Project [2014 – 2015]: Used a Generalized Linear Mixed Model to ascertain the overall effectiveness of Center’s wellness programs by evaluating improvement in patients’ quality of life.
- Evolutionary Biology Mutation Project [2014 – 2015]: Provided statistical consultancy for project evaluating effect of genetic mutation on a micro-organism’s survival outlook. Advised team on data collection methodologies and data governance, led functional data analysis of results and interpreted key insights to improve the experiment. [Mention key projects led or undertaken and specific contributions to each project. Include metrics here too, where possible.]
- Responsible for studying local Healthcare and Telecommunications markets and vendor operations to provide highly effective targeted advice on market entry and expansion strategies and generate quarterly market health projections.
- Deployed predictive models to forecast market sizes and advise vendors on optimal product mix and most effective marketing techniques for market penetration.
- Conducted primary research by liaising extensively with new clients to gather information on product types and quantities; maintained strong working relations with clients to establish dominant position in existing markets.
- Master of Science (Honours) | Statistics | University of Australia | Australia | 2013
- Bachelor of Science (Honours) | Statistics and Actuarial Science | University of Australia | 2012
- Languages: Fluent in English.
- Availability: 1 Month’s Notice
References available upon request
About the Data Scientist Role
By working closely with business stakeholders, data scientists develop an understanding of business goals, and determine how data can be used to achieve those goals. They design data modeling processes, create algorithms and predictive models to extract and extrapolate the data needed by the business. They then help analyse the data and share their insights with stakeholders.
The data scientist role is one that melds the disciplines computer science, modeling, statistics, analytics, math, plus a strong business acumen. After all, data scientists are responsible for estimating the unknown – they uncover answers to major questions, helping businesses make strategic decisions.
What is the Difference between Data Scientist and Data Analyst?
Both data analysts and data scientists play key roles in helping businesses make objective decisions informed by data. But here’s the difference.
Data scientists, as mentioned above, predict the unknown by designing data modeling processes, creating algorithms and predictive models, and performing custom analysis. On the other hand, data analysts examine large data sets to identify trends and patterns, derive insights, and present their insights visually.
What Hiring Managers Look Out For in Data Scientist Resumes
The realm of data science is arguably one of the hottest industries today. Data-focused careers are highly coveted by job seekers, and skilled data scientists are well sought after by employers.
To stand out as a data scientist:
- You’ll need a strong educational background, holding at least a Master’s Degree in Data Science, Statistics, Computer Science, Mathematics or Engineering related fields. While there are exceptions, employers tend to look at your academic background as proof that you have the depth of knowledge and skills required to process and analyse data.
- Solid business acumen is a must for a data scientist. You’ll need to understand your company’s industry and its operations, before you can derive meaningful insights from data to solve abstract problems or predict future events.
- Support achievements in your resume with metrics. As a data scientist, it is imperative that you use your own data to give recruiters and employers a sense of scale of your past projects or career achievements.
- Indicate your hard skills and technical skills – specifically your proficiency in analytical tools, technologies and programming languages. Key ones for data scientists include machine learning, data visualization, R, Python, SQL, Tableau and Spark.
- It may be tempting to flood your resume with metrics. After all, data scientist roles deal with large volumes of data, right? But don’t forget to extract insights from your metrics! Use numbers to show employers concretely how you have analyzed data and drawn relevant insights to drive business results.
- Show that you can communicate effectively with diverse stakeholders and departments. Data scientists collaborate in cross-functional teams, and work closely with business stakeholders. So, you must be able to understand business goals well, and present insights in ways that are easily understood by all stakeholders.
Data Scientist Job Opportunities
Job Hunting Tips from our Resident Headhunter
- Create a winning cover letter and send it along your resume. The cover letter will serve as an introduction about yourself and is a great venue to answer the question “Why should we hire you?”
- Be active on social media, particularly on LinkedIn. Make sure to create a strong online profile that represents you and you professional experiences. Here’s our comprehensive guide on writing a great LinkedIn profile.
Before You Go…
Be sure to download this resume sample, which uses our tested-and-proven resume writing techniques, as a guide for your own CV. Best part is, this sample is available for free 🙂
And before you start sending out applications, send us your resume for a free CV feedback analysis from our team. We’ll review your CV in detail, share personalised feedback on its strengths and weaknesses, and show you how you can improve it.