Data Science Architect (ML/NLP) – Referral
Company Name:-
QuEST Global Engineering
Job Location:-
Thiruvananthapuram, Kerala
Job Summary:-
Data Science Architect (ML/NLP) – ReferralResponsibilities:
Work independently, or as part of a team, to design and develop Advanced Analytics solutions using machine learning algorithms and data visualization techniques.
Closely working with Executives, Senior Managers, and coordinate with Client, cross-functional team to understand the business objectives and deliver as per the business needs.
Team player in an agile work environment to drive data model implementations and algorithms into practice.
Responsible for defining and documenting architecture, capturing and documenting architectural requirements, preparing estimates and defining technical solutions to proposals (RFPs).
Solving complex problems involving multiple data sets, as well as optimizing existing machine learning libraries and frameworks
Migration of existing ML models from one platform/language to another based on requirements.
Running tests, performing statistical analysis, and interpreting test results and documenting machine learning processes.
Keeping up-to-date in developments in data science specifically machine learning, NLP and data visualization
Playing a key role in mentoring for Data Science Learning path, Project guidance & support and play the role of a CoC leader.
Contribute to Branding, Client value, Collaboration through design thinking and Agile practices
Required Skills (Technical Competency):
Expertise and experience on statistical data analysis such as transforming business requirements into Analytical models, Designing Algorithms, Predictive modelling, and Strategic solutions that scales across massive volumes of data
Experience in Text Analytics, developing different statistical machine learning models, new forecasting models, and data mining solutions to various business problem.
Strong experience in Text Mining of cleaning and manipulating text and finding sentiment analysis from text mining data.
Experience in foundational machine learning models and concepts: regression, random forest, classification, clustering, boosting, stacking, blending, GBM, PCA, LDA, SVD, NNs, HMMs, CRFs, MRFs, deep learning algorithms like RNN, autoencoders.
Interpret problems and provides solutions to business problems using data analysis, d
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