DATA SCIENCE ENGINEER SKILLS, EXPERIENCE, AND JOB REQUIREMENTS

Published: October 4, 2024 - The Data Science Engineer applies advanced quantitative skills and statistical analysis to develop key performance metrics and models. Expertise in machine learning, SQL, and big data tools like Hadoop and Spark enables the handling and optimization of complex data architectures. This role is crucial for deriving actionable insights from extensive, diverse data sets.

Essential Hard and Soft Skills for a Standout Data Science Engineer Resume
  • Statistical Analysis
  • Machine Learning
  • Programming
  • Data Management
  • Big Data Technologies
  • Database Management
  • Data Visualization
  • Data Wrangling
  • Predictive Modeling
  • Cloud Platforms
  • Analytical Thinking
  • Communication
  • Project Management
  • Attention to Detail
  • Creativity
  • Teamwork
  • Adaptability
  • Problem-Solving
  • Critical Thinking
  • Time Management

Summary of Data Science Engineer Knowledge and Qualifications on Resume

1. BS in Information Technology with 4 years of Experience

  • Expertise in the design and development of SSIS packages
  • Experience with data warehouse development e.g. building fact and dimension tables with complex transforms and type 1 and type 2 changes
  • Experience with SQL Server SSAS and data cubes.
  • Knowledge of best practices around indexing and query performance
  • Experience working with clients throughout all phases of a development project
  • Explore relevant data through visualization and statistical methods
  • Good knowledge of programming and statistics.
  • Excellent code writing abilities.
  • Experience in Data-mining and yield analysis.
  • Experience in developing application and data-source in Hadoop big data platform 

2. BA in Economics with 3 years of Experience

  • Experience in a data-centric industry role
  • Experience with data wrangling (R, Python for data processing)
  • Experience with optimizing technical processes and SQL or NoSQL databases
  • Strong verbal and written communication skills
  • Eagerness to learn new technical concepts
  • Strong analytical skills and attention to details
  • Experience developing Node.js applications.
  • Knowledge of financial markets with trade and settlement lifecycle 
  • Knowledge of BI tools, such as Tableau, Microsoft PowerBI, or Qlik.

3. BS in Computer Science with 5 years of Experience

  • Relevant experience in a data engineering environment
  • Extensive hands-on experience in cloud data ecosystems, covering data ingestion, data modeling, and data provisioning to consumers and downstream systems
  • Excellent coding skills in relevant languages (e.g. SQL, Python, C#, Scala). 
  • Experience in ETL/ELT design, data and interface specifications, quality assurance and testing methods. 
  • Deep knowledge of data pipeline orchestration (e.g. ADF) and distributed computation frameworks (e.g. Azure Synapse, Spark)
  • Experience in implementing DataOps and MLOps concepts
  • Proven track record of delivering value from data. 
  • Experience in building robust, scalable, high-quality data products in iterations and integrating them into existing and new data-pipelines
  • Experience with agile methodologies in a professional development environment (CI/CD)
  • Fluent in English, German language skill 

4. BS in Data Science with 3 years of Experience

  • Experience with one or more of the following: high-level programming languages such as MATLAB, SQL, Python, FORTRAN, or C
  • Ability to work and communicate effectively with diverse teams of engineers, scientists, and technicians.
  • Experience with Machine Learning and working with extremely large raw binary data file sets (100 GBs or more)
  • Experience with interfacing with and integrating modern process control historical data
  • Working experience in the development of software with parallel threads of direct data access using high and low-level file I/O functions as well as database-SQL access
  • Experience in scripting with tools such as Python, SQL, and Bash.
  • Experience with Data Analytics tools such as Pandas, Numpy, and Elasticsearch.
  • Experience with dashboard and charting tools such as Grafana, Tableau, and Matplot
  • Demonstrable data analysis project experience, either academic or professional
  • A practical, organized, and pragmatic approach to work.
  • Willingness and ability to contribute to process improvement initiatives.

5. BS in Statistics with 4 years of Experience

  • Experience working in a data analytics or data engineering role.
  • Can use python or R for data analysis, and take pride in writing production quality code
  • Excellent communication skills and excel at communicating data-driven insights to technical and non-technical audiences.
  • Experience with BI tools such as Looker or Tableau 
  • Experience with massively parallel processing frameworks such as Spark or Hadoop 
  • Experience with data querying languages (e.g. SQL), scripting languages (e.g. Python), and/or statistical/mathematical software (e.g. R)
  • Knowledge and experience in statistical and data mining techniques: GLM/Regression, Random Forest, Boosting, Trees, text mining, social network analysis, etc.
  • Ability to implement statistical models for forecasting, time series predictions
  • Experience using web services: REST API, SOAP, WCF etc. 

6. BS in Applied Mathematics with 3 years of Experience

  • Excellent oral and written communication skills.
  • Experience in software development using Python, Java, Scala or other programming language.
  • Experience in software development with SQL.
  • Experience or familiarity with Cloud ecosystem (AWS, Azure, or GCP).
  • Familiarity with Big Data technologies such as Hadoop, MapReduce, Spark.
  • Familiarity with DevOps tools and technologies such as GIT, Jenkins, Docker, Nexus/Artifactory and CI/CD pipeline.
  • Strong analytical and problem solving skills.
  • Desire and ability to learn other programming languages and software development tools.
  • Experience or familiarity with JavaScript, web platforms and services.
  • Understanding of designing and implementing RESTful APIs.
  • Knowledge of data warehousing design concepts, various data management systems (structured and semi-structured) and integration with various database technologies (Relational, NoSQL).

7. BS in Electrical Engineering with 7 years of Experience

  • Strong quantitative analytical skills, with statistical analysis competence in techniques such as correlation, regression, KPI metric development, segmentation modeling and algorithms.
  • Experience in machine learning models such as Deep Neural Networks, XGboost and random forest.
  • Advanced working knowledge of SQL (writing and debugging)
  • Proven track record developing, managing, and optimizing big data architectures and pipelines
  • Experience performing internal and external root cause analysis
  • Strong analytical skills when working with unstructured data sets
  • Experience working with cloud-based data solutions, in particular GCS.
  • Proven experience successfully manipulating, processing, and extracting value from large and disconnected data sets
  • Experience with automation and configuration management
  • Strong background in Big Data tools such as Hadoop, Kafka, and Spark, Kubernetes

8. BS in Software Engineering with 2 years of Experience

  • Ability to look at things differently, debug, troubleshoot, design, and implement solutions to complex technical issues.
  • Excellent understanding of statistical or machine learning techniques, such as clustering, regression, time series forecasting, tree-based methods, sampling methods
  • Strong technical and analytical abilities, a knack for driving impact and growth, and experience with programming/scripting in a language such as Java or Python.
  • Experience in machine learning programming including Tensorflow.
  • Basic understanding of statistical programming in a language such as R, Python, or SAS, SPSS, MATLAB.
  • Basic understanding of Cloud (AWS, Azure, etc.)
  • Excellent verbal and written communication skills.
  • Work or internship experience using data science tools in a corporate environment.
  • Interest in, understanding of, or experience with Design Thinking and Agile Development Methodologies
  • Demonstrated ability using scientific computing libraries, such as NumPy, Pandas, SciPy, Scikit-learn, Matplotlib, and Plotly
Relevant Information
Data Science Analyst Skills, Experience, and Job Requirements