Job Description

Position Overview

We are seeking a skilled and analytical

Data Analyst

to join our team. You will be responsible for analysing payment transactions, financial assets, user behaviour, and market data to support risk control, product strategy, customer segmentation, and business decision-making.

Using machine learning models and data warehouse technologies, you will help build data-driven systems and insights across multiple financial domains. This role is ideal for candidates with strong technical skills in

Python, R, SQL, Power BI, Hive, Hadoop

, and experience with financial modelling and ETL pipelines.

Key Responsibilities

1. Data Collection & Processing

Collect, clean, and organise financial, transactional, and user behaviour data to ensure accuracy. Develop ETL pipelines using Python, SQL, PL/SQL, Kettle, Sqoop, etc. Maintain data warehouse layers (ODS, DWD, ADS, RP) and support financial DWH design. Automate data tasks and scheduling using Linux shell scripts.

2. Data Analysis & Modelling

Use machine learning algorithms (KNN, K-Means, XGBoost, LightGBM, Random Forest, ANN, RNN) for predictive modelling and data mining. Build financial models such as churn prediction, customer behaviour preference, risk identification, and asset forecasting. Perform feature engineering, model tuning, and evaluation using AR, KS, Recall, Lift, AUC metrics. Implement algorithms using Python (Pandas, NumPy, SciPy, Scikit-learn) or R.

3. Data Visualization & Reporting

Create dashboards and visual reports using Power BI, FineBI, FineReport, or similar. Support BI teams in developing internal system visualisation pages. Present insights, market analysis, and model results in clear business-friendly reports.

4. System Integration & Optimization

Integrate with transaction systems, risk control engines, and data APIs. Participate in data warehouse optimisation and database tuning (MySQL, PostgreSQL, Oracle, DM8). Work with big data frameworks including Hadoop, Hive, and MapReduce. Ensure secure data handling, encryption, and compliance.

5. Data-Driven Decision Making & Innovation

Support product optimisation, customer segmentation, and fraud prevention. Apply NLP sentiment analysis to market or crypto asset datasets. Explore innovative AI + FinTech applications (robo-advisory, asset forecasting, credit scoring).

QualificationsEducation & Experience

Bachelor's degree or above in Statistics, Mathematics, Computer Science, Data Science, Financial Engineering, or related field.

Minimum 2 years of experience

in data analysis or data science, preferably in financial services or securities.

Technical Skills

Strong proficiency in

Python, R, SQL, PL/SQL

. Familiarity with major databases: Oracle, MySQL, PostgreSQL, DM8. Hands-on experience with data warehouse design and ETL development. Skilled in Power BI, FineBI, Tableau, or similar visualization tools. Understanding of the Hadoop ecosystem (Hive, Sqoop, MapReduce). Knowledge of ML algorithms and model evaluation methods. Experience in NLP, sentiment analysis, or time-series analysis is a plus. Comfortable working in Linux environments and using Shell scripts.

Personal Attributes

Strong logical thinking and high sensitivity to data patterns. Ability to communicate complex results clearly to non-technical teams. Excellent collaboration skills and cross-functional communication. Passionate about FinTech, AI applications, and continuous learning.

Bonus / Preferred Experience

Experience in banking, securities, payments, blockchain, or health data analytics. Data warehouse architecture or BI system integration experience. Experience with customer profiling, churn prediction, or market behaviour analysis projects. Participation in Kaggle competitions or research publications.
Job Type: Full-time

Pay: Up to 35,000.00 per year

Ability to commute/relocate:

London SW1H 0NB: reliably commute or plan to relocate before starting work (preferred)
Application question(s):

What is you expected salary for this role?
Work authorisation:

United Kingdom (preferred)
Work Location: In person