What’s in it for you:

Ability to understand problem statement and implement analytical solutions & techniques independently being proactive and thought leadership aligning with the Data Science and Modelling team strategy.
To be able to convert excel model into python models.
To be able to conceptualize, design, and deliver high-quality framework solutions.
To leverage technology and integrate new transformed datasets into the data pipeline architecture.
To work closely with various stakeholders and key technology partners to understand data, collect, clean, transform, validate, and visualize datasets.
To identify and manage dependencies and risks across various analytics datasets, products, and applications.
To collaborate and coordinate with internal teams and establish operational assistance to provide analytical support of various commodity datasets.
To be able to understand market data and apply business metadata to new and existing pipeline data flows and help us deliver market relevant analytics content to internal users, external users and products.
To be able to execute with discipline and deliver appropriate documentation.
To be able find, investigate, resolve, and report issues to the internal and external users.
Maintain new and existing data change/ transformation request backlog to ensure all requests are tracked and completed.
Prioritize backlog and ensure our datasets are accurate and up to date
Fast learner: ability to learn and pick up a new tool/ platform quickly.

Required Requirements:

University degree in Science, Mathematics, Economics, Computer Science, Information Technology, Statistics or equivalent.
Proven experience with Data automation using analytical skills.
Ability to retrieve, interrogate, manipulate, and analyse data and presenting finding.
Ability to create efficient solutions to complex problems.
Strong knowledge and experience working with databases (MS SQL Server, PostgresSQL).
Excellent communications and strong presentation skills to the wider audience is essential.
Experience of working and leading processes in agile work streams driving data quality and improvements, experience of Visual Studio (VSTS) desirable but not essential.

Expert level proficiency in Python, Spark and SQL.
Proficiency in, or willingness to learn, the other language – R or Matlab.
Experience of working on end-to-end data science pipeline: problem scoping, data gathering, transformation, Exploratory data analysis (EDA), extracting insights, visualizations, monitoring and maintenance.
Knowledge of machine learning, probability theory, statistics, algorithms, NLP and Deep Learning.
Experience in Unified analytics platforms like Databricks is beneficial.
Knowledge of deep learning and related toolkits: Tensorflow, PyTorch, Keras, etc