We are looking for a Data Scientist with strong capabilities in data cleaning, data quality management, data mining, statistical analysis, and business insight generation.

The role will be responsible for transforming complex and fragmented business data into reliable datasets and actionable insights. The successful candidate will work across CRM, trading platforms, payment systems, KYC systems, customer service systems, and other internal data sources.

This is not a pure reporting or Power BI role. The key focus is to first ensure that the underlying data is accurate, consistent, and usable, and then apply data analysis and data mining techniques to identify business patterns, risks, opportunities, and operational improvements.

Key Responsibilities

1. Data Cleaning & Data Quality

Clean, standardise, validate, and reconcile large datasets from multiple internal systems.
Identify missing, duplicated, inconsistent, abnormal, or incorrectly mapped data.
Investigate root causes of data discrepancies across CRM, trading systems, payment systems, KYC systems, and other data sources.
Develop automated data-quality checks and validation rules.
Identify incorrect client, account, sales, IB, group, and transaction mappings.
Establish data-quality monitoring mechanisms and exception reporting.
Work with IT and Data teams to resolve recurring upstream data issues rather than relying only on manual correction.

2. Data Mining & Business Analysis

Analyse large datasets to identify patterns, trends, correlations, anomalies, and business opportunities.
Perform client segmentation based on behaviour, trading activity, funding patterns, profitability, engagement, and lifecycle.
Identify high-value, inactive, high-risk, abnormal, or potentially unprofitable client segments.
Analyse Sales, IB, client, trading group, and regional performance.
Identify factors affecting client conversion, retention, deposit behaviour, trading activity, and profitability.
Conduct cohort analysis, behavioural analysis, funnel analysis, and trend analysis.
Support management in identifying key business drivers and areas requiring intervention.

3. Trading & Brokerage Data Analysis

Analyse data including, but not limited to:

Client registration and KYC
FTD and subsequent deposits
Deposits and withdrawals
Trading volume and trading frequency
Client P&L
Account equity and balance
Sales commissions
IB rebates
Client acquisition channels
Trading account groups
Product usage
Client retention and inactivity
Risk and abnormal trading behaviour
Sales and IB profitability

4. Statistical Modelling & Advanced Analytics

Build statistical or machine-learning models where appropriate to support business decisions.
Develop client scoring and segmentation models.
Support churn prediction, client value prediction, profitability analysis, and behavioural classification.
Identify abnormal data patterns and potential operational or business risks.
Develop models to support client lifecycle management and resource prioritisation.
Evaluate model accuracy and ensure outputs can be practically applied by business teams.

5. Data Definition & Governance

Work with Operations, Risk, Finance, Sales, Compliance, Product, IT, and Data teams to establish consistent metric definitions.
Document business rules, data logic, field definitions, and calculation methodologies.
Assist in developing data dictionaries and data lineage documentation.
Ensure management reports use consistent and validated data definitions.
Support establishment of data ownership and data governance processes.

6. Reporting & Decision Support

Translate complex analysis into clear business conclusions and recommendations.
Build reusable datasets and analytical models for management reporting.
Support BI teams in preparing reliable data sources for Power BI or other reporting tools.
Produce ad hoc analyses for management on business performance, customer behaviour, profitability, and operational risks.
Present findings clearly to both technical and non-technical stakeholders.

Technical Requirements

Strong SQL skills are required.
Strong Python skills for data processing and analysis, including experience with libraries such as Pandas, NumPy, SciPy, or equivalent.
Strong experience in data cleaning, transformation, and exploratory data analysis.
Good understanding of relational databases and data modelling.
Experience working with large and complex datasets.
Strong statistical analysis capabilities.
Experience with machine learning and predictive modelling is preferred.
Experience with Power BI, Tableau, or similar BI tools is an advantage, but is not the primary focus of the role.
Experience with ETL/ELT processes, data warehouses, or data pipelines is an advantage.

Preferred Experience

3–7 years of experience in Data Science, Data Analytics, Data Mining, Data Quality, or related roles.
Experience in financial services, fintech, brokerage, trading, payments, or similar industries is highly preferred.
Experience analysing client behaviour, transactions, financial data, or trading data is highly desirable.
Experience working with CRM and multiple interconnected business systems is preferred.
Strong ability to investigate data problems independently and identify root causes.

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