Resume Skills and Keywords for Machine Learning (ML) Engineer
Machine learning programmers create AI software that runs on its own to automate predictive models for recommended searches, virtual assistants, translation applications, chatbots, and driverless autos. They create machine learning systems, use algorithms to provide correct predictions, and troubleshoot data set issues.
Skills required for an ML Engineering role:
- Data Structures
- Data Modeling
- Predictive Modeling
- Data Visualization
- Predictive Analysis
- Statistical Modeling
- Data Mining
- Clustering & Classification
- Quantitative Analysis
- ML Algorithms
- Web Scraping
What recruiters look for in an ML Engineer resume:
- Skills in designing machine learning systems and self-running artificial intelligence (AI) software to automate predictive models.
- Ability to transform data science prototypes and apply appropriate ML algorithms and tools.
- Competency in ensuring that algorithms generate accurate user recommendations.
- Experience in turning unstructured data into useful information by auto-tagging images and text-to-speech conversions.
What can make your Machine Learning (ML) Engineer resume stand out:
A strong summary that demonstrates your skills, experience, and background as an Machine Learning (ML) Engineer
- Machine learning engineer with proven success in building successful algorithms and predictive models for different industries. Highly adept at clustering and classification, we scrapping, data analysis, and visualization. A thriving analyst with the ability to apply ML techniques and algorithm development to solve real-world industry problems.
Targeted job description
- Understand and use computer science fundamentals, including data structures, algorithms, computability and complexity, and computer architecture.
- Use exceptional mathematical skills, in order to perform computations and work with the algorithms involved in this type of programming.
- Produce project outcomes and isolate the issues that need to be resolved, in order to make programs more effective.
- Collaborate with data engineers to build data and model pipelines.
Sample Resume of Machine Learning (ML) Engineer in Text Format
8769502883 | email@example.com | linkedin.com/in/kiransharma
Machine learning engineer with proven success in building successful algorithms and predictive models for different industries. Highly adept at clustering and classification, we scrapping, data analysis, and visualization. A thriving analyst with the ability to apply ML techniques and algorithm development to solve real-world industry problems.
Machine Learning Engineer ► Stack Intellect, Bangalore Nov 2017 - May 2021
- Used machine learning and statistical modeling techniques to develop and evaluate algorithms to improve performance, quality, data management and accuracy
- Worked with Adobe’s research teams to take the state of the art in research and make great products and features
- Developed, simulated, tested, and improved algorithms for predicting electrical load and generation
- Designed lean proofs of concepts (POC) to answer targeted business questions. Explored and worked with a wide range of proprietary, interesting data stores. Applied existing methods or developed new methods.
Machine Learning Engineer ► Network Corp, Bangalore May 2015 - Oct 2017
- Consulted with managers to determine and refine machine learning objectives.
- Designed machine learning systems and self-running artificial intelligence (AI) software to automate predictive models.
- Turned unstructured data into useful information by auto-tagging images and text-to-speech conversions.
- Solved complex problems with multi-layered data sets, as well as optimized existing machine learning libraries and frameworks.
- Developed ML algorithms to analyze huge volumes of historical data to make predictions.
Data Visualization | Predictive Analysis | Statistical Modeling | Data Mining | Clustering & Classification | Quantitative Analysis | ML Algorithms | Web Scraping
- Certificate in Advanced Data Science
- Course in Python at Coursera Jan 2013 - Jun 2013
- Course in C++ at Coursera Sep 2013 - Feb 2014
English | Hindi