IronVest, a fast-growing consumer security and privacy startup, is hiring an Applied Machine Learning Engineer / Data Scientist, focused on high-performance and concurrent machine learning applications for production systems.

Your role as a data scientist will be to interpret the raw data and extract valuable meaning out of it by using machine learning and using this information to find patterns and develop solutions for biometric security and other use cases that the organization needs to grow. You’ll combine practical skills like coding and math with the ability for statistical analysis to achieve results.

What you’ll do

• Using machine learning to select features, create and optimize classifiers
• Carrying out pre-processing of structured and unstructured data
• Enhance data collection procedures to include all relevant information for developing analytic systems
• Processing, cleansing, and validating the integrity of data to be used for analysis
• Analyzing large amounts of information to find patterns and solutions
• Developing prediction systems and machine learning algorithms
• Collaborate with Engineering, Business, and Product teams
• Propose solutions and strategies to tackle business challenges

Qualifications & Skills

• Strong Software Engineering Background in Python
• Proven Experience as Data Analyst or Data Scientist
  • Strong Math Skills (Multivariable Calculus and Linear Algebra) - Understanding the fundamentals of Multivariable Calculus and Linear Algebra is important as they form the basis of a lot of predictive performance or algorithm optimization techniques.
  • Machine Learning – Good knowledge of machine learning methods like k-Nearest Neighbors, Naive Bayes, SVM, Decision Forests, and neural networks.
  • Statistics – Good applied statistical skills, including knowledge of statistical tests, distributions, regression, maximum likelihood estimators, etc. Proficiency in statistics is essential for data-driven companies.
• Problem-solving aptitude
• A degree in Computer Science, Engineering or a relevant field is preferred