Data scientist with ML, NLP

Position description
We are looking for a candidate with experience as a data scientist for machine learning and natural language processing problems. As a data scientist you will develop and implement advanced ML and NLP algorithms to analyse large data sets, extract insights and develop new products and solutions for our analysts, investor clients and management.

You are expected to have a blend of technical expertise, a deep understanding of the theoretical underpinning of ML and NLP and their practical applications, an interest and aptitude for real life business use cases and creativity and ingenuity.

You will have proficiency in data mining, data structuring, model development, validation and testing. You will work with multiple teams including domain experts, SMEs, product owners, management, developers and data analysts to understand the business needs and to help find ways to translate our complex datasets into actionable data-driven strategies, actions and products. You will have a proven track record for applying ML and NLP techniques to actual, real-world problems for measurable and real life impact.

Responsibilities:
• Develop and implement ML and NLP models to analyze and interpret complex datasets.
• Data mining and pre-processing to prep datasets.
• Evaluate model performance and iterate to improve efficiency and accuracy.
• Communicate findings and insights to technical and non-technical business stakeholders.
• Participate in the entire life cycle of ML/NLP projects from inception to deployment including and up to necessary support and post deployment validations fully implement best practices in data science and machine learning.
• Collaborate with cross functional teams to identify and solve relevant business problems.
• Present findings to senior audiences.

Required skills:
• 3 to 5 years of experience in data science rule with ML and NLP exposure
• Strong programming skills in Python common proficiency with ML/NLP libraries
• Experience in data mining preprocessing, model development
• Solid knowledge of a diverse set of machine learning techniques and their real-world practical applicability
• Strong statistical and analytical skills
• Experience with cloud computing services
• Ability to effectively communicate complex data insights to non-technical stakeholders is critical.

Helpful/ desirable skills:
• Basic understanding of financial research or some experience in the financial services industry is highly preferred but not mandatory.