UsefulBI Corporation - Senior Engineer - Machine Learning (8-15 yrs)

Overview :

UsefulBI is looking for highly skilled candidates with expertise in generating powerful business insights from verylarge datasets, aiming to enable needle-moving business impacts through cutting-edge statistical analysis.

We arelooking for passionate Data Scientists/ML Engineer who can envision the design and development of analyticalmodels that can support strategic and tactical decision-making.

The candidate should be well-versed in statistics, R and SAS languages, machine learning techniques and mathematics.

Python is also the most widely used programming language across the board when it comes to data science.

Furthermore, problem-solving, analytical knowledge ofdatabases will also be a great asset. Experience with AWS is a plus.

Experience Required :

- Minimum 8+ year's experience in Data Science.

- Must have good knowledge and experience in Python.

- Must have good knowledge of R and SAS.

- Must have good experience with machine learning frameworks (e.g., TensorFlow, Keras, Scikit-learn).

- Experience with AWS (Bedrock) is a plus.

- Typically requires relevant analysis work and domain-area work experience.

- Expert in the management, manipulation, and analysis of very large datasets.

- Superior verbal and written communication skills, ability to convey rigorous mathematical concepts and

considerations to non-experts.

Key Responsibilities :

- Create and maintain optimal data pipeline architecture.

- Develop custom data models and algorithms to apply to data sets.

- Use predictive modeling to increase and optimize customer experiences, revenue generation, ad targeting, and other business outcomes.

- Analyse large amounts of information to discover trends and patterns.

- Build and deploy machine learning models into production environments.

- Assess the effectiveness and accuracy of new data sources and data gathering techniques.

- Coordinate with different functional teams to implement models and monitor outcomes.

- Develop processes and tools to monitor and analyse model performance and data accuracy.

- Work with stakeholders including the Executive, Product, Data, and Design teams to assist with data-related technical issues and support their data infrastructure needs.

- Create data tools for analytics and data scientist team members that assist them in building and optimizing our product into an innovative industry leader.