Remote - Data Scientist : Boston MA- Hybrid

Role:  

Data Scientist

Employment Type:  

Contract

Work location:

Boston  Location in USA – Currently 2 days from site and likely to get extended for all days.  

Work mode   :

Hybrid

About the Role

About the Role:

We are seeking an experienced and highly skilled Data Scientist  to lead the development and implementation of advanced data modeling and predictive analytics to support investment strategies.

The ideal candidate should have extensive experience in data science designing and deploying statistical modelling with at least 6 years of hands-on experience in Financial/Investment / Asset Management / Fixed Income.

Job Responsibilities 

•Utilize data analytics and advanced statistical techniques to inform investment decision-making and drive business growth.

•Collaborate with cross-functional teams to identify opportunities for leveraging data to enhance investment strategies.

•Develop and maintain a robust data infrastructure to support data-driven decision-making.

•Stay updated on industry trends and best practices in data science and investments, and implement new techniques and tools as needed.

•Lead the development and implementation of advanced data modeling and predictive analytics to support investment strategies.

•Provide strategic guidance and recommendations to senior leadership on investment opportunities and risks based on data analysis.

•Oversee the creation and maintenance of data dashboards and reports to track investment performance and identify areas for improvement.

•Collaborate with other departments to ensure alignment and integration of data-driven strategies across the organization.

Mandatory Skills 

•Primary Skills : Has experience with data science, statistical modeling and time series analysis of financial markets data, Fixed Income Assets, Investment Banking.

•Secondary Skills : Has experience in Gen AI , Language Model etc.

Required Education

·       Bachelor’s degree in computer science or a related field.

·       Competencies typically acquired through an advanced degree (in Statistics, Mathematics, Data Science or other relevant field of study)

·       Proficiency in the mathematics underlying Machine Learning

·       Ability to present complex technical information in a clear and concise manner to a variety of audiences.

Required Experience

·       Minimum 14+ years of experience in data science designing and deploying statistical modelling with at least 6 years of hands-on experience in Financial/Investment / Asset Management / Fixed Income.

·       Has experience with data science, statistical modeling and time series analysis of financial markets data, Fixed Income Assets and/or Investment Banking.

·       Has experience in Gen AI , Language Model etc.

·       Worked for asset management firms in roles aligned to the front office.

·       Has advanced knowledge of python and/or R

·       Asset Allocation, Research, quat engineering

·       Broad experience with hands-on modelling and DS lifecycle activities, particularly in Python, and ability to communicate at a high level with DS and non-technical leaders

·       Knowledge of graphical packages such as plotly or gg plot

·       Excellent understanding of Statistical modeling, Machine Learning (Supervised, Unsupervised, Recommendation engines, Optimization etc.)  Deep Learning (RNN, CNN, LSTM, Auto encoders, GANS etc.), Natural Language Processing techniques and algorithms

·       Strong coding experience in Python, Pyspark, Keras/tensorflow

·       Experience with SQL & at least one NoSQL databases

·       Experience of working on any of cloud platform (AWS, Azure, IBM etc.)

·       Work closely with data engineers to source, analyze and engineer features, collaborate with fellow data scientists to develop, test and deploy Machine Learning models

·       Exposure to creation of data pipelines to engineer apps deployment (CI/CD) across platforms and environments and working with ML pipelines

·       Demonstrate analytical and problem-solving skills, particularly those that apply to the big data environment