Senior Data Scientist - FinTech

- We have a fantastic opportunity for a Senior Data Scientist with a our global client within FinTech. Looking for one who enjoys the challenge of taking BIG DATA and creating a business solution that affects thousands of lives.

- As a Data Scientist you will work with R&D team, your key responsibilities include researching, designing and implementing data models and statistical algorithms to analyse diverse sources of high dimensional and fast moving data to build continually learning systems.

Responsibilities :

- Use vast amounts of online transaction data to develop predictive models that increase sales and improve online customer experience to predict customer performance

- Develop statistical modeling /data mining / machine learning algorithms / NLP and provide expert recommendations on solutions & applications of advanced analytics.

- Collaborate with our client's internal business partners and R&D to define, design and develop intelligent algorithms, statistical models to operate on massive data sets.

- Leverage experience with machine learning and statistical programming to define, design, and build projects that leverage our data

- Leverage tools, processes and procedures allowing an organization to create, manipulate, and manage very large data sets/ stat

- Develop and document prototypes to simulate the behavior of a new technique

Skills & Experience :

- Bachelors / Masters degree or PhD in Stats / Math / Machine Learning / Information Data Science/ Computers / Analytics / Statistics

- Minimum 4-5 years of demonstrated data experience using R, SAS, Python etc including applied techniques in data mining, machine learning, or graph mining preferably with in Banking & Financial.

- Develop, evaluate and refine scoring systems for Risk & Fraud

- Experienced with statistical modeling/data mining algorithms (deep knowledge of the fundamentals - not just the mechanics) - regression, support vector machines, decision trees, time series, clustering, CHAID, ensemble methods (random forest, bagging, boosting etc.)

- Thorough understanding on a scripting language (e.g. Python, Ruby, Perl) for fast prototyping and significant knowledge of core library and common design patterns.

- Knowledge with open source data processing frameworks.