Ideal Candidate

• The ideal candidate will come with hands-on experience as a Data Scientist having a Data-oriented personality with a good scripting and programming skills. He/she should have hands-on expertise on data science toolkits, specifically in using R for statistical modeling and machine learning.
• Hands-on expertise and exposure in R packages, Shiny for interactive Web applications  & machine learning algorithms, model building, statistical modelling, predictive modelling environments. This person will have a proven track record of delivering successful technology solutions to business clients preferably in financial services.

Requirements

• 5-8 years of experience in R (preference), Python, or SAS for complex data manipulation, statistical analysis, and machine learning.
• Experience with Forecasting, Time Series Analysis, statistical modeling, etc.
• Experience translating high-level project requirements into technical tasks.
• Data manipulation and data engineering experience involving structured and unstructured data.
• Data manipulation expertise involving data extractions, data matching between multiple systems, transformations, cleansing, and loading.
• Proficient in using Shiny to build interactive web apps straight using R.
• Experience with big data and cloud platforms especially GCP.
• Experience in developing predictive, prescriptive, optimization, and forecasting models.
• Experience in interpreting results from statistical and mathematical models.
• Experience in advance data visualizations and interpretation.
• Experience with data visualization tools (e.g., Tableau, Bokeh, D3) or elastic stack (Kibana).
• Experience with modern machine learning libraries, including Keras, TensorFlow, PyTorch, MXNet.
• Natural Language Processing (NLP) experience a plus.
• Collaborative and decisive with strong communication and interpersonal abilities.
• Expertise in working with large and complex datasets to create dashboards and data visualizations
• Strong analytics background: Hands on experiences in statistical modeling (e.g. regression model, test and control etc.), machine learning, etc.
• Multitasker: Good project management and communication skills that can work multiple projects at same time using minimum guidance.

Responsibilities

• Understand business requirements to translate business problems into analytics problems and construct analysis road-map based on the business context
• Manage large volumes of structured and unstructured data, extract & clean data to make it amenable for analysis
• Analyse big data using statistics, econometrics, mathematics, operations research, and text mining techniques
• Develop good visualization to communicate business insights from analysis and make actionable recommendations
• Help deploy analytics solutions and enable tracking of business outcomes to measure return on investment
• Keep up with cutting edge analytics techniques and tools in the continuously evolving area of decision science
• Present/Advise/Interpret/Justify on analytics solutions from a project to Client/Internal Stakeholder