Data Scientist

3 days ago

Singapore The Edge Asia Full time

Our client is a premier financial institution with a strong presence within APAC region and their Financial Crime Analytics team is looking for a Data Scientist.

The role is a permanent position based in Singapore.

EA Licence Number: 16S8131
Recruiter Licence Number: R22104669

**Some of the key responsibilities will include**:

- 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 Machine Learning 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 leverage predictive models to optimize customer experiences.
- You will be creating automated anomaly detection.

**To be eligible for this role you will require**:

- Minimum 3 years of experience in the data science field with a total experience of 5 years in data, technology, and analytics space.
- Bachelors degree or above 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, R or Pyspark and SQL
- 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.

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