Data analytics role with a global bank.

Role Responsibilities :

- You will be building Statistical/Machine Learning models in either Python, R or PySpark to solve business problems using Advanced Analytics in Predictive and Prescriptive space.

- You will be presenting results of your analysis and interpreting outputs of your models to give recommendations to the stakeholders.

- You will be maintaining and documenting produced code and modelling process.

- You will be working with data engineers to prepare and integrate data from various data sources.

- You will be helping data engineers and ML engineers with model deployment.

- You will be cooperating with (both technical and non-technical) various Bank departments to adjust analytics solution to the business needs.

- You will be performing data mining, exploration, and analysis.

- You will be creating data visualizations, reports, dashboards, and data audits.

- You will Design, train, and implement machine learning algorithms.

- You will leverage predictive models to optimize customer experiences.

- You will be creating automated anomaly detection.

Our Ideal Candidate :

- Minimum 5 years of experience in the data science field with a total experience of 10 years in data, technology, and analytics space.

- Bachelors or Advanced degree in engineering, finance, accounting, mathematics, or social sciences with excellent quantitative methodological skills.

- Solid experience in at least one of the following programming languages: Python, and SQL.

- Experience in application of statistics, classification, regression, segmentation, and dimensionality reduction methods and preferably in any of the following data science related areas Econometrics, time-series analysis, optimization methods, Bayesian statistics, natural language processing.

- Experience in creating and using advanced machine learning algorithms. Linear regression, logistic regression, decision and regression trees, random forest, boosting algorithms (e.g.: XGBoost), k-means, neural networks, hierarchical clustering, and principal component analysis.

- Solid understanding and implementation knowledge of the following data science concepts

- Bias-variance trade-off, regularization, model evaluation metrics, cross-validation, bootstrapping, hyperparameter tuning, feature selection & feature engineering.

- Experience in relational databases (Hadoop/HIVE) using SQL

- Knowledge of data science toolkits such as R, NumPy, and MatLab.

- Experience in data visualisation.

- Expertise in data mining and machine learning.

- Working knowledge of statistical models and business intelligence.

- Strong English oral and written communications skills and experience defending research findings.

Nice To Have :

- Experience with version control systems (e.g. GIT/Bitbucket).

- Experience in Cloud technologies.

- Experience in ML Ops.

- Experience with ETL and data engineering.

- Experience with Spark and other big data technologies.

- Knowledge of graph databases and knowledge graph applications in data science is a plus (e.g. Neo4j).

- Working knowledge in Fraud Risk Management, Conduct and Financial Crime Compliance domains in Bank.

This job opening was posted long time back. It may not be active. Nor was it removed by the recruiter. Please use your discretion.