Machine Learning Engineer

Location: Turbigo

Role overview

The candidate will be a key person for advanced analytics usage within DP&A by promoting Machine Learning (ML) approaches and maintaining/improving the current Artificial Intelligence (AI) end to end infrastructure with the aim to maximize the values of data and push toward the evolution of new use cases thanks to the adoption of the Big Data ecosystem.

Main activities

Design ML systems to automate models training, prediction and performance validation

Working on prototypes, experiment new approaches and test several solutions on ML algorithms and tools application

Industrialize end to end solutions by defining and applying best practices in ML Ops field

Analyze huge volumes of historical data to make predictions and trends forecasting

Maximize the value of available data by proposing new potential use cases on top of heterogeneous datasets, raw data and complex models with the application of descriptive, predictive and prescriptive analysis techniques

Responsibilities

Research and implement appropriate ML algorithms and tools, design ML systems, propose AI applications according to requirements and data science prototypes

Select appropriate datasets, design efficient data models and data representation methods

Understanding business objectives and propose/developing models that help to achieve them, along with metrics to track their progress

Managing available resources such as hardware, data, and team staffing so that project deadlines are met Design and deploy to production end to end ML pipelines by using best practices for monitoring/retraining/redeploying

Analyzing/ranking ML algorithms that could be used to solve a given problem and perform statistical analysis and fine-tuning according to test results

Define the preprocessing or feature engineering to be done on a given dataset, analyze models underperformance and design strategies to overcome them Skills required

Proficiency with ML frameworks, libraries, data structures, data modeling, and software architecture

Knowledge of Cloud environments with experience on GCP Platform Cloud-based solutions (e.g. Big Query, Cloud Pub/Sub, AI Platform, GCS, Cloud Functions)
Advanced proficiency with Python code writing and basic libraries for ML and data science (e.g. Num Py, Pandas, Scikit-learn, Tensor Flow)
Expertise in visualizing and manipulating big datasets

Knowledge of SQL/No

SQL language/DB: querying, modeling and data structures

Knowledge of maintenance and ongoing development of continuous build/integration infrastructure

Excellent analytical and problem-solving abilities with in-depth knowledge of mathematics and statistics

At least two years’ experience as a ML engineer and Proficiency with English language

Master's degree in computer science, data science, mathematics, or a related field.
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