Machine Learning Engineer

Key Skills

Java Programming, Analytical and Problem solving, Python Programming, SQL, Customer Support, Big Data Technology, Machine learning techniques, Data science techniques, Deep Learning, NumPy, Pandas, XGBoost, Apache Hive, Scikit-learn, spaCy, Natural Language Toolkit - NLTK, matplotlib, Data Modelling, SPARK Programming, RNNs, CNNs, Random forests, Data Analysis

Job Description

Barclays is a British universal bank. We are diversified by business, by different types of customers and clients, and by geography. Our businesses include consumer banking and payments operations around the world, as well as a top-tier, full service, global corporate and investment bank, all of which are supported by our service company which provides technology, operations and functional services across the Group.

Risk and Control Objective

Ensure that all activities and duties are carried out in full compliance with regulatory requirements, Enterprise Wide Risk Management Framework and internal Barclays Policies and Policy Standards.

Working Flexibly

We’re committed to providing a supportive and inclusive culture and environment for you to work in. This environment recognises and supports ways to balance your personal needs, alongside the professional needs of our business. Providing the opportunity for all our employees, globally to work flexibly empowers each of us to work in a way that suits our lives as well as enabling us to better service our customers’ and clients’ needs. Whether you have family commitments or you’re a carer, or whether you need study time or wish to pursue personal interests, our approach to working flexibly is designed to help you balance your life.

If you would like some flexibility then please discuss this with the hiring manager.

Introduction:

As a Machine Learning Engineer you will be involved into various machine learning activities starting from data discovery, exploratory data analysis & solving business problems from an analytics standpoint & using machine learning models as needed. You should also have the capability to take these Models into production with best practices & monitoring model & improving Models further to stay useful to business. This role would require to have data engineering skills using Python & Spark.

What will you be doing?

• Ability to convert a business problem into a data science/machine learning problem
• Strong in solving business problems using classification and regression machine learning techniques
• Good working knowledge in Logistic Regression, Decision Tree, Random Forest, GBT, XGBOOST, Support Vector Machine, Linear Regression
• Should have good exposure and understanding in time series Modelling using ARIMA, ARIMAX
• Exposure into how to handle under fitting and overfitting
• Should be capable of applying techniques which helps to generalize Models
• Regularization techniques LASSO, RIDGE & ELASTIC NET and when to apply these
• Strong in various feature engineering techniques and when and how to apply these
• Good exposure in Unsupervised machine learning like clustering, dimensionality reduction, Outlier detection
• Ability to understand how Models are optimized using various techniques including Gradient Descent approach
• Good understanding of deep learning algorithms CNN, RNN, LSTM and how to control overfitting in such cases
• Good hands on in data engineering to process huge scale of data using Big Data (Spark/Hive)
• Good coding practices to write production ready code for creating data pipeline for Models to consume
• Very good hands on in python (Pandas/Numpy/Scikit-Learn/NLTK/spaCy/Matplotlib)

What we’re looking for:

• 4+ years of experience in data science or postgraduate in analytics/machine learning and demonstrated machine learning application to multiple use cases using Python & Spark
• Strong in supervised & un-supervised machine learning. Well versed with classification type of problems using Random Forest, Gradient Boosted Trees, XGBOOST, SVM, logistic regression
• Should have worked on regression technique like linear regression. Also sound understanding of various regularization techniques such as LASSO, RIDGE & ELASTIC NET & Time Series Modelling.
• Should have sound understanding of various generalization techniques like Ensemble, stacking
• Very good hand on in Python (Pandas/Numpy/Scikit-Learn/NLTK/spaCy/Matplotlib)
• Good hand on in data engineering skills using Big Data (Spark/Hive)
• Very good SQL experience & knows how to write optimized queries

Skills that will help you in the role:

• Should have core machine learning skills like supervised and unsupervised using Python and on Spark cluster (using pySpark).
• Good knowledge on time series modelling knowledge like ARIMA, stationarity test etc.
• Should have sound understanding of deep learning using KERAS & Tensorflow
• Good data engineerign skills
• Knowledge of DevOps & Model deployment framework

Where will you be working?

Pune

Be More at Barclays

At Barclays, each day is about being more – as a professional, and as a person. ‘Be More @ Barclays’ represents our core promise to all current and future employees. It’s the characteristic that we want to be associated with as an employer, and at the heart of every employee experience. We empower our colleagues to Be More Globally Connected, working on international projects that improve the way millions of customers handle their finances. Be More Inspired by working alongside the most talented people in the industry, and delivering imaginative new solutions that are redefining the future of finance. Be More Impactful by having the opportunity to work on cutting-edge projects, and Be More Valued for who you are.