VPS Securities is seeking a highly skilled and motivated Senior Data Scientist to join our Data team. In this role, you will work on large-scale data projects to generate insights, design predictive models, and develop machine learning and deep learning solutions that directly enhance business performance, customer experience, and strategic decision-making.
1. Responsibilities:
Business Problem Solving & Modeling
- Analyze complex business problems and user behaviors to identify trends and actionable insights.
- Apply statistical and machine learning techniques to develop models that support data-driven business decisions.
Data Exploration & Feature Engineering
- Work with large-scale datasets, perform data cleaning, transformation, and feature extraction tailored to different modeling needs.
- Conduct exploratory data analysis (EDA) to uncover patterns and evaluate data quality.
ETL & Data Pipeline Development
- Design and build end-to-end ETL pipelines to integrate, preprocess, and prepare data for analytical use cases.
- Ensure efficient and scalable data workflows aligned with model requirements.
Advanced Modeling & Algorithm Development
- Build and deploy machine learning and deep learning models using frameworks like TensorFlow, PyTorch, and PySpark.
- Solve complex problems such as customer segmentation, churn prediction, recommendation systems, and cross-sell/up-sell targeting.
Experimentation & Model Validation
- Run A/B tests and conduct rigorous model validation to ensure business applicability and performance accuracy.
- Collaborate with cross-functional teams to monitor and fine-tune models post-deployment.
2. Qualifications:
Education:
- Bachelor's degree or higher in Data Science, Computer Science, Applied Mathematics, Information Technology, Telecommunications, Finance, Banking, Statistics, or related fields.
Experience:
- Minimum of 2 years of hands-on experience with Machine Learning, Deep Learning, and applied AI solutions.
- Prior experience in the finance, securities, or banking sector is highly preferred.
Technical Skills:
- Solid understanding of machine learning techniques such as linear regression, decision trees, random forest, boosting, SVM, PCA, and clustering algorithms (e.g., k-means).
- Proficiency in deep learning architectures including MLP, CNN, RNN, LSTM.
- Strong background in statistics, linear algebra, calculus, data structures, graph theory, and database systems.
- Expertise in Python (preferred), R, Java, Scala, and strong SQL skills.
- Experience with ML/DL frameworks such as scikit-learn, TensorFlow, Keras, PyTorch, Spark MLlib, H2O, etc.
- Experience with big data tools (e.g., Hadoop, Spark) is a plus.
Other Skills:
- Strong problem-solving mindset, critical thinking, and attention to detail.
- Excellent communication skills and ability to translate technical results into actionable business insights.
- Ability to work collaboratively in a cross-functional and dynamic environment.