Senior Data Analyst
About the Role
A member of the squad who is responsible for delivering business value from data. They achieve this by analyzing data, presenting data insights and reports with business application, developing data led improvements to products, translating business needs and requirements into predictive analysis, functionalities and software specifications, driving true value to our customers; bridging the gaps between business, data and delivery teams. Responsibilities:
What to expect:
- Data driven improvement - You possess an excellent understanding of the business and/or market combined with the technical understanding of the data models supporting current products/services that allows you to proactively facilitate the conversation between stakeholders on how to improve products and solutions creatively using data and then to deliver it. You research domain knowledge and learn techniques to improve the business relevance of your work.
- Predictive analysis - You have a good understanding of Data Science tools and techniques and their relative strengths and weaknesses. You support the creation of predictive analysis and help to interpret the results.
- Analysis and assurance - Through iterative collection, analysis and visualization of data, you can easily explain complex matters to deliver understanding and assurance of product behaviour working as one team with stakeholders.
- Data Analytics Strategy - You advise on how data might best be presented, structured and standardized. You contribute to the improvement of data analytics systems and platforms.
- Collects interprets and analyses external & internal information, assessing the relevance for the business and identifying opportunities, risks and problems within the business solution.
- Prepare reports and presentations on results of research and/or analysis identified. Present results to squads and wider as appropriate. To document the outcome of analysis and/or research.
- Based on analysis and research, prepare recommendations for further investigation or translate results into proposals/recommendations for action to develop of new functionality/changes to product or new products and services.
- Analyses specified problems and issues to find the best solutions.
- Designs, understands and contributes to the testing of products/services using test cases/sets of regression tests to assess against defined features, functionality, and/or impact. Analyses output to describe behaviour of a product to provide assurance of the behaviour and highlight gaps with ideal behaviour, recommending data driven actions for increasing the solution value.
- Produces and maintains conceptual models, prototypes and supports their implementation.
- Provides advice and contributes to the designing of new functionalities and products.
- Leads or supports the creation of machine learning algorithms by applying standard statistical analysis and data preparation methods.
- Ensures continuous management of changes, new requirements and product specifications facilitating back-log management.
- Builds and maintains relationships with clients and other relevant stakeholders to understand needs and requirements and makes sure they are consulted and informed when needed.
- Produces reports and further information to enhance the 'explain-ability' of the product behaviour.
- Keeps abreast of new developments regarding their area of expertise and market trends, shares it to all relevant stakeholders through documentation. Examines technology trends, discusses and recommends technical developments to improve products.
- Supports users on deployed/live systems.
- Shares gained knowledge amongst team members, bridging gaps between customer-facing and technical roles.
What will make you successful?
- Bachelor in Computer Science, Engineering (quantitative), Mathematics, Physics, Statistics, or a quantitative field. Candidate with Master qualifications or research experience a strong plus.
- Excellent verbal and written communications.
- Proven experience in using multiple data science methodologies in solving complex business problems - analytical mind and business acumen.
- Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.)
- Strong understanding and background in applying statistical and data mining techniques: GLM/Regression, Random Forest, Boosting, Trees, text mining, social network analysis, etc.
- Experience in applying one or more AI/ML libraries: PyTorch, Keras, Tensorflow, Prophet, scikit-learning, etc.
- Adept at data manipulation and processing tools: Python (generic, Numpy, Pandas), R, SQL, Hadoop stack, Spark.
- Good understanding of big data technologies: NoSQL, MapReduce, stream and batch processing, Dataflow model, Kafka.
- Familiar with data visualization stack: Tableau, Microstrategy, ggplot2, D3, matplotlib, Seaborn.
- Experience in deploying solution to production and comfortable with DevOps, MLOps, CI/CD, API practices.
What we offer click here SWIFT - Make your impact click here You may like to know the team better by knowing the people in the team. Review LinkedIn profile of the people on the list below : Caroline DOZIN - Data Analyst Chapter Lead, SWIFT What we offer
- Minimum 5-7 years of experience in data analytic/science
- Applicable research experience.
- Strong background in one or more area: time-series analysis, NLP, product analytic, risk analytic, fraud detection, anomaly detection
- Comfortable with vast datasets and ability to reveal insights from data fast
- Experience in Agile methodology.
We put you in control of career
We give you a competitive package
We help you perform at your best
We help you make a difference
We give you the freedom to be yourself We give you the freedom to be yourself. We are creating an environment of unique individuals - like you - with different perspectives on the financial industry and the world. An environment in which everyone's voice counts and where you can reach your full potential regardless of age, background, culture, colour, disability, gender, nationality, race, religion , or veteran/military status.