Background information about the job or company (e.g., role context, company overview)

Tezza Business Solutions Ltd is a "niche” service provider of Software Development, Quality Assurance and Software Testing services. Tezza began its operations as Web Development company in 2000 in Overland Park, Kansas. Since then, we’ve evolved into a Services-oriented company who only engage in Product development as a value-add service to our custome...

Responsibilities or duties

The candidate will apply data mining techniques and conduct statistical analysis to large, structured and unstructured data sets to understand and analyse phenomena. Model complex business problems, discovering insights and opportunities through statistical, algorithmic, machine learning and visualisation techniques, working closely with clients, data and technology teams to turn data into critical information used to make sound business decisions. Execute intelligent automation and predictive modelling.

Qualifications or requirements (e.g., education, skills)

Bachelors Degree in Information Technology/ Information Studies and/or any other relevant course

Proven development experience in software and software engineering.

Understanding of financial services data processes, systems, and products.

Experience in technical business intelligence.

Knowledge of IT infrastructure and data principles.

Project management experience.

Exposure to governance and regulatory matters as it relates to data.

Experience in building models (credit scoring, propensity models, churn, etc.).

Experience needed

Candidate should have 3 – 5 years of experience:

Working with unstructured data (e.g. Streams, images)

Understanding of data flows, data architecture, ETL and processing of structured and unstructured data.

Using data mining to discover new patterns from large datasets.

Implement standard and proprietary algorithms for handling and processing data.

Experience with common data science toolkits, such as SAS, R, SPSS, etc.

Experience with data visualisation tools, such as Power BI, Tableau, etc.

Proficiency in application and web development. Structured and Unstructured Query languages e.g. SQL, Qlikview; SSIS SSRS, Python,JSON , C#, Java, C++, HTML

Any other provided details (e.g., benefits, work environment, team info, or additional notes)

Competent in Machine Learning programming in R or Python, with supplementary still in Matlab, Java, etc. Familiar with the Hadoop distributed computational platform, including broader ecosystem of tools such as HDFS / Spark / Kafka

Liaise and collaborate with the Data Science Guild, providing support to the entire department for its data centric needs.

Collaborate with subject matter experts to select the relevant sources of information and translates the business requirements into data mining/science outcomes.

Presents findings and observations to team for development of recommendations.

Acts as a subject matter expert from a data science perspective and provides input into all decisions relating to data science and the use thereof.

Educate the organisation on data science perspectives on new approaches, such as testing hypotheses and statistical validation of results.

Ensure ongoing knowledge of industry standards as well as best practice and identify gaps between these definitions/data elements and organisation data elements/definitions.

• Gathering of data for use in Data Science models, ensuring that chosen datasets best reflect the organisations goals.
• Performs data pre-processing including data manipulation, transformation, normalisation, standardisation, visualisation and derivation of new variables/features.
• Utilises advanced data analytics and mining techniques to analyse data, assessing data validity and usability; reviews data results to ensure accuracy; and communicates results and insights to stakeholders.
• Designs various mathematical, statistical, and simulation techniques to typically large and unstructured data sets in order to answer critical business questions and create predictive solutions which drive improvement in business outcomes.
• Drives analytics and insights across the organisation by developing advanced statistical models and computational algorithms based on business initiatives.
• Codes, tests and maintains scientific models and algorithms; identifies trends, patterns, and discrepancies in data; and determines additional data needed to support insight. Processes, cleanses, and verifies the integrity of data used for analysis.
• Use data profiling and visualisation techniques using tools to understand and explain data characteristics that will inform modelling approaches. Communicate data information to business with various skill levels and in various roles, presenting trends, correlations and patterns found in complicated datasets in a manner that clearly and concisely conveys meaningful insights and defend recommendations.
• Creates, maintains and optimises modelling solutions that enable the forecast of quality data outcomes.
• Ensures that volumetric predictions are modelled so that resource requirements are optimally considered.
• Develops and maintains optimal evaluation techniques to ensure that modelled outcomes are rigorous and creates model performance tracking, and drives sustainable and effective modelling solutions.
• Develops, implements, monitors and maintains a comprehensive operational IA plan, rules, methodologies and coding initiatives in order to drive IA for remediation efforts.
• Develops and co-ordinates a comprehensive strategy for productionalising automation software so that it is accurate and well maintained
• Mines data using state-of-the-art methods. Enhances data collection procedures to include information that is relevant for building data models.
• Provides input into Data management and modelling infrastructure requirements and adheres to the organisations’s infrastructure development processes, including the management of User Acceptance Testing (UAT).
• Ensure business integration through integrating model outputs into end-point production systems, where requirements must be understood and adopted relating to data collection, integration and retention requirements incorporating business requirements and knowledge of best practices.
• Builds machine learning models from and utilises distributed data processing and analysis methodologies.

• Machine Learning programming in R or Python
• Matlab, Java
• Hadoop distributed computational platform
• HDFS / Spark / Kafka
• Data mining techniques
• Statistical analysis
• Algorithmic techniques
• Machine learning techniques
• Data visualisation techniques
• Intelligent automation
• Predictive modelling
• Data pre-processing (manipulation, transformation, normalisation, standardisation, derivation of new variables/features)
• Data validity and usability assessment
• Mathematical techniques
• Simulation techniques
• Computational algorithms
• Scientific models and algorithms (coding, testing, maintenance)
• Data profiling
• Communication of data insights to business stakeholders
• Modelling solutions optimisation
• Volumetric predictions
• Evaluation techniques for model outcomes
• Model performance tracking
• Operational IA plan development and maintenance
• Automation software productionalisation
• Data collection procedures enhancement
• Data management and modelling infrastructure requirements input
• User Acceptance Testing (UAT) management
• Business integration of model outputs
• Distributed data processing and analysis methodologies
• Subject matter expertise in data science
• Hypothesis testing
• Statistical validation of results
• Knowledge of industry standards and best practices

• Bachelors Degree in Information Technology/ Information Studies and/or any other relevant course
• Proven development experience in software and software engineering.
• Understanding of financial services data processes, systems, and products.
• Experience in technical business intelligence.
• Knowledge of IT infrastructure and data principles.
• Project management experience.
• Exposure to governance and regulatory matters as it relates to data.
• Experience in building models (credit scoring, propensity models, churn, etc.).
• Working with unstructured data (e.g. Streams, images)
• Understanding of data flows, data architecture, ETL and processing of structured and unstructured data.
• Using data mining to discover new patterns from large datasets.
• Implement standard and proprietary algorithms for handling and processing data.
• Experience with common data science toolkits, such as SAS, R, SPSS, etc.
• Experience with data visualisation tools, such as Power BI, Tableau, etc.
• Proficiency in application and web development.
• Structured and Unstructured Query languages e.g. SQL, Qlikview; SSIS SSRS, Python,JSON , C#, Java, C++, HTML