Job description 

WHAT YOU’LL DO

As a Senior Machine Learning Engineer, you will play a fundamental role on technical decision making, embedded in projects and working with clients of different industries. You will also get your hands dirty writing code to get Machine Learning models working at real world production environments. This role teams up closely with Data Scientists, Impact Leaders, Consultants and Team Leaders to boost, deepen and streamline the development of innovative solutions that respond to high complexity problems.

 

In this role you will:

• Provide data science services to Bain case teams and clients worldwide.
• Work with other data scientists and case teams to assess client demands and suggest data science methods that provide practical, value-added answers to the client and case teams.
• Develop solutions that bring critical insights to wide scale of different problems such as targeting customers and segmenting markets, product design, marketing optimization, demand forecasting and brand valuation, profit and price analyses, and fraud detection.
• Develop, prototype and test machine learning algorithms on data sets that can range from a few data points to billions.
• Apply machine learning and statistical techniques including regression models, decision trees, random forests, gradient boosting, support vector machines, clustering and topic models.
• Prepare various sources of data using data wrangling methods in Python, R and SQL, leveraging infrastructure including Cloud computing solutions and relational database environments.
• Keep abreast of new and current statistical methodologies, machine learning and data wrangling techniques.
  
   

ABOUT YOU

• Solid theoretical background on machine learning, ideally practical experience on building and deploying machine learning models.
• Write code in Python, embracing good code practices.
• Being able to deal with different tasks during the whole Machine Learning life cycle. What kind of tasks? ML model deployment and maintenance, building and using tools for ML monitoring, CI/CD pipeline design, supporting experimentation with ML models, designing and building data pipelines, among others.
• You have experience working with cloud infrastructure.
• You are familiar with the lifecycle of a Machine Learning model.
• You are familiar with Containers.
• You can build a service that exposes a ML Learning model to be consumed by other applications.
• You love teaching others and sharing good practices.
• You like getting your hands dirty with new tools and languages. Sometimes it is better to take a risk and learn something new, rather than choosing the good old hammer you always use. It doesn’t matter if you take more time, it is an investment for the future.
• You like being versatile. You get bored when every day looks the same, so you like sometimes getting dressed as a Data Scientist, Data Engineer or Software Engineer.
• Constant collaboration with your teammates.
• Provide and accept frequent feedback.
• Be autonomous within a collaborative environment.
• Find the best and unique version of yourself and boost it!
• You have 1 or more years of experience and/or a relevant graduate degree.
• A Master’s Degree or Ph.D. in in a quantitative discipline such as Statistics, Mathematics, Engineering, Computer Science, Econometrics, Business Analytics, or Market Research. Previous work experience in a related field is a plus.
• Strong foundation in Mathematics and Statistics
• Proficiency with data wrangling, visualization and modeling in either R or Python is required
• Proficiency in SQL is a plus
• Examples of domain expertise in which we are interested is: predictive modeling, churn analysis, time series forecasting, computer vision, text mining, recommender systems, market basket analysis, segmentation, graph analytics, natural language processing and text analytics
• Experience with Git and modern software development workflow is a plus
• Strong interpersonal and communication skills are pre-requisite for this role
• Ability to explain and discuss mathematical and machine learning technicalities to a business audience
• Must thrive in a fast-paced environment
• Travel required (40-60%)