Analyst, Data Science Resume Sample
Anastacio Schmeler
64863 Nona Lock,
San Francisco,
CA
+1 (555) 276 2433
Work Experience
Senior Analyst, Data Science
02/2017
-
PRESENT
Boston, MA
- Previous experience in machine learning and algorithm development is required
- Build, test and implement reports with accurate, easy to read, useful information that satisfies company and client needs
- Collaborate with internal stakeholders, data stewards, team managers, engineers and developers to scope, design, and implement new data products, reports, and dashboards
- Utilize proprietary and third-party tools (web analytics platforms and ad-serving technologies) to report and analyze audience, product, and revenue data
- Model complex problems, discover data patterns, share insights, and identify opportunities using statistical, algorithmic, data mining, and visualization techniques
- Ensure that quality and timeliness of measurement deliverables meet company and client expectations
- Work closely with data engineering team to integrate data sets into data warehouse and align with architecture and computing environment
- Take ownership in maintaining the existing data processes and products and ensure that all stakeholders have appropriate access to relevant data sources
- Participate in intra- and interdepartmental meetings to develop, test, and enforce reporting standards, prioritize projects and discuss data objectives across several business functions
Analyst, Data Science
11/2013
-
09/2016
Dallas, TX
- Writing production-ready code that allows for effective data analysis
- Creating data visualizations in web-based dashboards (e.g., Tableau, Data Studio, etc.)
- Experience or understanding of DoubleClick (DART), Google’s tech stack, or other ad-serving technologies and supporting analytical tools
- Knowledge of various web-based measurement tools (audience and campaign) and the ability to wrangle data from different sources/interfaces
- Excellent presentation and report-writing skills, and the ability to communicate key insights to upper-level management
- Interpret data and analyze results to generate insights and formulate the strategic implications to clients
- Deliver analyses such as: advertising performance analysis, website analytics, customer segmentation, survey design and analysis, ROI modeling, lifetime value analysis, cross channel analysis, media mix analysis and brand research
Global Insights Data Analyst Data Science Internship
10/2006
-
09/2013
Detroit, MI
- Act as a data steward and ensure data integrity by monitoring exception reporting, anomalies, and providing requirements for any system clean-ups
- Work alongside Director of Data Science and Strategic Insights teams to ensure cross-collaboration and learning
- Have strong attention to detail and the ability to relate results to client business objectives
- Showcase your curious, analytical mind and be a problem-solver
- 3 – 5 years of experience in data mining, BI applications for big data, and background in statistical applications or business intelligence tools for analysis and forecasting
- Use scientific methods to hypothesize, discover, validate, and Q/A various data sets, preferably in a media-related industry
- Working knowledge of Amazon Redshift or other big data clusters
- Proficiency in one or more of the following data query or programming languages: SQL, PostgreSQL, SAS, R, Python
- Proficient in building data models and efficiently querying large sets of data
Education
Upper Iowa University - Cedar Rapids Center
2001 - 2006
Science's Degree in Marketing
Professional Skills
- Using super strong technical skills to turn big data in Teradata and Hadoop into actionable insights
- Strong influencing and dealing skills with senior stakeholders
- Proven programming experience in at least two of the following: Python, Java, R
- Advanced programming skills to query and analyze complex data sets using statistical software packages and data manipulation (SAS/SQL, R, and/or Python)
- Proven track record of turning insights into product growth opportunities in prior roles with increasing scope and responsibilities
- Experience in a leading tech company with 5 to 7 years of experience
- Has healthcare experience with a payer, provider group/hospital, ACO, or consulting organization, or has previous analytics experience and capabilities
How to write Analyst, Data Science Resume
Analyst, Data Science role is responsible for programming, software, modeling, languages, java, finance, credit, training, integration, database.
To write great resume for analyst, data science job, your resume must include:
- Your contact information
- Work experience
- Education
- Skill listing
Contact Information For Analyst, Data Science Resume
The section contact information is important in your analyst, data science resume. The recruiter has to be able to contact you ASAP if they like to offer you the job. This is why you need to provide your:
- First and last name
- Telephone number
Work Experience in Your Analyst, Data Science Resume
The section work experience is an essential part of your analyst, data science resume. It’s the one thing the recruiter really cares about and pays the most attention to.
This section, however, is not just a list of your previous analyst, data science responsibilities. It's meant to present you as a wholesome candidate by showcasing your relevant accomplishments and should be tailored specifically to the particular analyst, data science position you're applying to.
The work experience section should be the detailed summary of your latest 3 or 4 positions.
Representative Analyst, Data Science resume experience can include:
- Excellent communication and consultative skills, with the ability to present results to both technical and non-technical audiences
- Demonstrate working knowledge of research methods by designing, executing, and analyzing the following types of research with assistance from manager
- A good understanding of machine learning techniques and tools including; Spark, TensorFlow and Azure
- Experience using tools such as Python, R, Matlab, SAS, SPSS or equivalent for statistical modelling of large data sets
- Familiar or experience with predictive modeling, univariate analysis and creativity with visualization of modeling results
- Experience using tools such as Python, R, Matlab, SAS, SPSS or equivalent for statistical modeling of large data sets
Education on an Analyst, Data Science Resume
Make sure to make education a priority on your analyst, data science resume. If you’ve been working for a few years and have a few solid positions to show, put your education after your analyst, data science experience. For example, if you have a Ph.D in Neuroscience and a Master's in the same sphere, just list your Ph.D. Besides the doctorate, Master’s degrees go next, followed by Bachelor’s and finally, Associate’s degree.
Additional details to include:
- School you graduated from
- Major/ minor
- Year of graduation
- Location of school
These are the four additional pieces of information you should mention when listing your education on your resume.
Professional Skills in Analyst, Data Science Resume
When listing skills on your analyst, data science resume, remember always to be honest about your level of ability. Include the Skills section after experience.
Present the most important skills in your resume, there's a list of typical analyst, data science skills:
- Eager to develop both analytical and technical skills required to effectively analyze large data sets
- Strong business problem solving skills. Modeling is typically the easy part in our work
- Effectively present model methodologies to internal working teams and clients, including executive-level stakeholders
- Apply advanced programming and critical thinking skills to
- Communicate analytics effectively to all levels of audience without confusing technical terms
- Beginner / Intermediate R skills
List of Typical Experience For an Analyst, Data Science Resume
1
Experience For Senior Analyst, Data Science Resume
- Assist in documenting data processes and assumptions during modeling process and scoring/dashboard creation
- Explain model diagnosis and evaluation results to stakeholders and help produce and deliver training materials on models and dashboards for underwriting
- Evaluate alternative data and methodologies to identify where improvements can be made to our lending and servicing models and business rules
- Work with cross-functional teams to implement, test and iterate data-driven recommendationsApply data science rigor to marketing
- Develop and iterate robust statistical models to measure, attribute and predict impact of marketing and media efforts across multiple channels
- Leverage statistical techniques to identify relationships, meaning and actionability of data Increase speed, impact and quality of data analysi
- Create and manage data foundation to ensure data integrity and rapid processing
- Design, develop, refine and maintain TFS credit default models using statistical software
- Develop Risk scoring models based on credit bureau attributes and internal data
2
Experience For Global Insights Data Analyst Data Science Internship Resume
- Work with cross-functional teams to implement, test and iterate data-driven recommendations
- Proactively mine and explore data to uncover meaningful insights for our clients
- Present insights and tactical recommendations backed by robust analysis and data
- Leverage advanced analytics tools for rapid data exploration and analysis
- Create scenario analysis and visualize data with interactive dashboards
- Knowledge of SQL, Excel, and Pivot Tables (required)
- Conceptual and practical understandings of relational databases, join types, and group by clauses
- Interest in work done by companies in the “LumaScape” chart
- Background in any of the following helpful: SSRS (SQL Server Reporting Services), BI Tools (Tableau, Cognos, QlikView, Spotfire, etc.), other programming languages, statistical analysis tools (R, Python, SPSS, SAS, STATA)
3
Experience For Lead Analyst, Data Science Resume
- Strong intellectual curiosity and ability to structure and solve difficult problems with minimal supervision
- Manages own workload to deliver quality deliverables that meet requirements
- Communicates to supervisor status of assignments
- Notifies supervisor of issues that impact productivity or ability to meet expectations
- Follows standards/uses standard toolsets
- Does not miss important items
- Checks work in to central repositories
4
Experience For Senior Data Analyst Data Science Resume
- A sense of curiosity about what makes the web tick, a desire to experiment and innovate with existing tools, and a need to figure out what’s next and plan for the future
- Identify, source, transform and join public, proprietary and internal data sources
- Model large structured and unstructured data sources (e.g. financial transactional, time-series, text, speech/audio and image)
- Hands-on experience applying a wide variety of statistical machine learning techniques to real world problems spanning analysis, predictive modeling and optimization on structured and unstructured data
- Design and manage experiments and pilots designed to test hypothesis or generate test and control observation data
- Pro-actively seek continuous improvement opportunities and support cross-functional projects with business stakeholders for implementation
5
Experience For Senior Analyst, Data Science & Analytics Resume
- Proficiency with SAS or other analytic tools, such as R or Python
- Proficiency the latest data visualization tools (e.g. SAS Visual Analytics) and database software
- Create model scoring processes in R or SAS and collaborate with IT to ensure code meets requirements to execute in real-time with UW platforms. Help maintain and troubleshoot code as needed
- Periodically refit models on refreshed datasets
- Quickly grasp new concepts and technologies and adapt to changes and demands in a dynamic environment
- Monitor performance of existing risk functionality (scorecards, forecasts, models, and strategies). Identify enhanced functionality and recommend improvements to leverage data science or analytics in credit risk management
- Design and implement champion/challenger strategies, perform simulations of impact on new strategies
- Use the latest analytical tools to programmatically extract, clean, and analyze large, disparate, disorderly data sets
- Assist in the computer implementation of models and strategies in our decision engine and other software environments, to support both batch and real-time decision support
6
Experience For Principal Analyst, Data Science Resume
- Collaborate across the Toyota enterprise; particularly within Risk Management, with Business Intelligence and Business Technology Solutions, and with Legal and Compliance and other partners
- Take responsibility for preparing data for analysis, review data preparation/ETL code and provide critical feedback on issues of data integrity
- Execute complex data science projects that have a significant impact on Whirlpool’s strategic imperatives
- Collaborate with product, engineering, and marketing teams across many Whirlpool business domains (IoT, IIOT, Supply Chain, Pricing and Promotions) to translate business needs into technical solutions
- Select and apply appropriate statistical, machine learning, and computing methods to large-scale, high-dimensional, and streaming data
- Develop analytical methods to drive data-driven decision making
7
Experience For Senior Analyst Data Science Resume
- Stay current with the latest research and technology and communicate your knowledge throughout the enterprise
- Pull large sets of customer data from J.Crew’s customer database; audit the quality of this data against other sources of data
- Maintain regular reports, like customer dashboards
- Field requests from all functional business areas
- Pull and analyze large data sets
8
Experience For Senior Analyst, Data Science Integration Resume
- Proficiency with SQL, R, Python, Big Data Tools like Hive and SPARK
- Deploying machine learning to uncover patterns in large scale data sets to identify customer behaviour correlations and convert in scalable growth opportunities
- Solid hands on programming experience with at least one of the standard data science tools, Python (Pandas, Scikit-Learn etc), R, Scala/Spark
- Experience in a mass data environment with analytics to drive revenue growth or manage risk/costs in the business
- Identifying new enhancement opportunities in data sources & machine learning toolsets to utilise for further business growth
- Identifying and building relationships with key stakeholders
- Demonstrating commercial acumen to explore business growth opportunity and present your strategies effectively to senior management and key stakeholders, and see them through to implementation
- Strong knowledge of some supervised and unsupervised machine learning methods, such as Regression methods (e.g. Ridge Regression, Lasso Regression, Logistic Regression),
- Experience in data modeling, data analysis, ETL, SQL querying, and relevant analytics tools (e.g. Tableau, Cognos, R, SAS)
9
Experience For Data Analyst, Data Science & Analytics Resume
- Detailed knowledge of advanced analytics modeling including several of
- Provide data science and modelling support for business development, lead qualification, strategic product development
- Be accountable for modelling output and results
- Serve as subject matter authority on AE data science capabilities, staying current on latest developments and trends
- Pro-Actively interacting with others
- Execute on data science and model development components of AE projects
- Help define and develop methodologies for analysis and modelling requirements of projects; establish innovative modelling approaches where needed (e.g. Machine Learning, Bayesian analysis and inference, complex networks, text analytics, etc.)
- Help set vision and resource allocation for data science & model development
- Liaise with the different Active Equities (AE) investment teams on project execution
10
Experience For Analyst Data Science & Analytics Intern Resume
- Play a hands-on role in client project execution – working in close collaboration with FMA engagement teams – by creating content, serving as a thought leader and providing solutions-based advice for our clients
- Work in close partnership with AE leadership including senior management, business development leads and practice areas heads
- Define and lead all analyses to support Innovation initiatives, methodology development, End to End Tool development/ implementation, standards and best practices
- Partners with Product Leadership in the development of new product ideations, including identification/evaluation of data sources, authoring methodologies, and building out product features
- Plays a key role in product development from beginning to end; including developing/running models, determination of the model application, and summarizing/presenting results
- Work with cross-functional teams to design, implement, and test new consumer measurement methodologies
- Identify and address risks for major quality escapes
- Serves as liaison between Client Service, Product Leadership, Operations, and Technology with regards to internal client projects
- Seek and make business case for opportunities where data science solutions can add value for US Claims, with exposure to present to Claims Senior/Executive Managers
List of Typical Skills For an Analyst, Data Science Resume
1
Skills For Senior Analyst, Data Science Resume
- Effectively communicate technical work to a wide audience is required
- Present complex analyses and insights effectively in simple terms
- Good experience with both descriptive and inferential statistics – ability to build basic prototype models
- Experience with statistical programming using SAS or R, and data manipulation in SQL or a related query language
- Professional experience in either cloud computing or machine learning
- An excellent communicator with little fear of asking questions and seeking the answers
- Experience building analytical pipelines e.g. to deploy machine learning models into production
- Experience with the Hadoop ecosystem, ideally CDH/Impala/Hive and/or traditional database systems, including advanced SQL querying
- Experience with data storage, such as relational databases and retrieval environments, and the ability to create a modeling dataset
2
Skills For Global Insights Data Analyst Data Science Internship Resume
- Experience in statistical modeling or data science role
- Works hard, takes initiative, operates in ambiguity, and manages competing deadlines
- Has a deep understanding of and experience with medical and/or pharmacy claims
- Experience with bundled payments (e.g., PROMETHEUS), measuring performance at the episode-level (e.g., ETGs, MEGs) and population-level (e.g., ACGs, DxCGs)
- Proficient in big data ecosystem such as Hadoop, Spark and Azure and strong modeling techniques with tools/languages such as R, Python, Scala and Java
- Begin to develop a strong cross-functional relationships and foster integration of knowledge
- Develop and apply creative solutions that go beyond current tools to deliver data-driven insights to high-priority scientific problems
- Experience in developing and implementing predictive models
3
Skills For Lead Analyst, Data Science Resume
- Strong working knowledge of data science and modelling tools (e.g. R; Python with numpy/ scipy/ sklearn/ pandas; MATLAB) with fluency in at least two
- Strong hands-on understanding of data structures and algorithms
- Experience in analytics and digital marketing
- Professional experience in the full range of data science models (e.g. machine learning; statistics; optimization)
- Relevant work experience
- Hands on experience with analytical tools, such as: R, Python (Numpy/Pandas), scikit-learn, CART, Stata, TreeNet, SAS, JMP
4
Skills For Senior Data Analyst Data Science Resume
- Proactive collaboration and effective communication are critical
- Background in ecommerce product experience
- Works collaboratively and pitches in for the greater good of the team
- Exposure or experience in software/API development
- Previous business experience
- 2~5 years cross-function experience
5
Skills For Senior Analyst, Data Science & Analytics Resume
- Broad industry knowledge – good knowledge of client needs
- Collaborate with research, creative and design teams to merge ‘science with art’, and help transform customer experiences
- Understands scope, priority and timelines of assigned work
- Software engineering experience in C/C++/C#/Java/Scala or similar object oriented or functional languages is highly regarded but not a prerequisite
- Experience with databases and the ability to manipulate and query large data sets required
- Striking a balance between strategic thinking and actual hands-on analyses using tools & packages such as SQL, R, Python, Tableau etc
- Understanding of digital advertising concepts (Conversion Rate, CTR, CPC, CPM, eCPM)
- Working knowledge of related data management tools and Big Data technologies such as Cassandra, Hadoop, Spark, or Hive
6
Skills For Principal Analyst, Data Science Resume
- Programming expertise with R/Python
- Machine learning: using computers to improve as well as develop algorithms
- Never losing sight of the big picture and connecting the dots to evaluate how the insights impact eBay’s ecosystem
- Develop advanced analytics models using appropriate statistical or machine learning methodology to support strategic, tactic and operational business decisions
- Assists in designing billing data roadmap to enable advanced analytics support
- Clarity in articulating results, sharing knowledge and ideas to internal customers, managers, and stakeholders
- Knowledge of predictive toolsets, including SAS, R, or Python
- Knowledge of statistical diagnostics of models, including measures of variable significance, measures of model fit, and measures of model stability
- Lead big data / predictive analytics / machine learning strategies and development of applications / tools for the enterprise
7
Skills For Senior Analyst Data Science Resume
- Conduct ad-hoc analyses as appropriate, including in-depth data-driven reviews of Criteo’s performance for specific clients in the Americas
- Identifies opportunities to improve existing conditions and processes
- Does not need to be told the same thing multiple times
- Analyze business processes and identify opportunities to generate value using analytics
- Identify and synthesize primary data for analysis relevant to addressing business questions
8
Skills For Senior Analyst, Data Science Integration Resume
- Design and execute user friendly business intelligence reports on key billing & payment operations performance
- Work independently or lead others during projects
- Work with project leader to profile underwriting data and 3rd party vendor data when available
- Refresh and use the advanced analytics models driving TFS’s Collections Treatment Optimization program
- The ability to package ideas and communicate analytical results in a logical, understandable and compelling way for both technical and non-technical audiences
- Displays professionalism, honesty, integrity, self-confidence, maturity, respect and conviction
- Knowledge of predictive analytic techniques, such as random forests, logistic regression, elastic net, or similar predictive analytic techniques
- Fit predictive models to meet business needs, and design and evaluate Generalized Linear Models
- Begin to develop broad-based business knowledge
9
Skills For Data Analyst, Data Science & Analytics Resume
- Work with colleagues in research and development to design, build, and implement state-of-the art scientific algorithms to support Janssen R&D initiatives
- Participate in project teams and interact closely with scientific colleagues across various disciplines
- Lead initiatives in the Advanced Analytics & Business Insights team including Advanced Analytics Research, Machine Learning / Artificial Intelligence, and Data Science Innovation
- Develop methodologies and approaches to investigate relationships between various internal and external unstructured datasets utilizing statistical / mathematical modeling, machine learning, cognitive and artificial intelligence
- Collect, analyze and interpret qualitative as well as quantitative data with statistical theories and methods
- Develop relationships and works closely with the Executive and functional business leaders to identify data science opportunities and enable those capabilities within their teams. Develop 'data stories' and communicate findings to stakeholders from the various data set research initiatives via data visualization and storytelling
10
Skills For Analyst Data Science & Analytics Intern Resume
- Champion the “Data Driven" culture / best practices and the intellectual curiosity to solve the previously impossible and to think differently and creatively about unmet needs in healthcare and their potential solutions
- Responsible for staying on top of analytical techniques such as machine learning, deep learning and text analytics as they rapidly evolve
- Build Machine Learning models to help optimize our operations globally
- Build statistical models (regression, segmentation) to identify driver/ rider trends and run experiments across the world to help continuously improve our business
- Hands-on experience of using machine learning algorithms in real-world applications
List of Typical Responsibilities For an Analyst, Data Science Resume
1
Responsibilities For Senior Analyst, Data Science Resume
- Comfortable with managing tasks efficiently through to completion
- Work with Data teams across the company (i.e. product, engineering, finance) on cross-functional projects
- Enthusiastic about learning new tools & technologies
- Evaluate, recommend, and develop statistical and machine learning solutions for a diverse range of Credit Risk and Residual Value projects
- Discover new paths for growth for our clients through rigorous analysis of consumer data
- Help optimize budget and programs through test-and-learn, discipline, innovation and analysis to get the best possible return on our clients’ marketing investments
2
Responsibilities For Global Insights Data Analyst Data Science Internship Resume
- Identify key client customers for retargeting and advertising
- Proficiency in various statistical techniques, such as: Logistic Regression, Time Series, Experimental Design, Generalized Linear Models, Mixed Modeling, Multivariate Statistics, Large-Scale Predictive Modeling, CHAID/decision trees, Neural Networks, Monte-Carlo, Survival Analysis, Ensemble Models
- Apply lateral thinking in engineering predictive models, analytical solutions, and ad hoc decision support to statisticians, data scientists, and business groups
- Background in investment research is ideal
- Demonstrable experience applying data science approaches in relevant contexts, working with large and potentially unstructured data sets: anomaly or fraud detection; classification; clustering
3
Responsibilities For Lead Analyst, Data Science Resume
- Working knowledge of machine learning algorithms such as Random Forest, SVM, neural networks, etc. and/or Natural Language Processing techniques is required
- Proficiency with one or more programming languages such as Python, R, C++, or Java is required
- Delivering insights on existing Product & Tech initiatives, discovering innovative product growth opportunities based on insights, and creating momentum through influence
- Proficiency in most areas of mathematical analysis methods, statistical analyses, predictive modeling and/or machine learning (such as neural network, random forests, gradient boosting and time series models etc.) and in-depth specialization in some areas
- Working knowledge of analyzing administrative medical, pharmacy claims, and/or EMR data and clinical data; comfortable working with multiple data sources in both structured data and unstructured format to create analytic solution
- Analytical and curious, especially about how numbers can define company strategy
4
Responsibilities For Senior Data Analyst Data Science Resume
- A high-energy, results oriented individual
- Engineer innovative solutions alongside business groups to improve predictive accuracy, automate forecast delivery, and provide decision support across multiple levels of management
- Design, develop, and refine predictive models integrated into new and existing operational solutions
- Wrangle data from internal and industry sources to develop, leverage, and enhance analysis and predictive modeling
- Use the latest analytical tools to programmatically extract, clean, and analyze large, disparate, and disorderly data sets
- Excited by challenges
- Lead development of algorithms and cognitive capabilities that interact with other software and analytical components that run autonomously with minimal human supervision. Lead the research and build machine learning / predictive algorithms, pattern recognition and artificial intelligence capabilities to determine patterns, trends, and insights from very large, complex data
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