Work as the data strategist with business stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions.
Proactively look for unsolved, data-rich, high-impact business problems and create new opportunities in the AI solutioning pipeline.
Evaluate feedback and challenging issues gathered by the business and technical teams and transform them into tractable problems to resolve.
Participate in research paper publishing, Applied AI Competitions; organize hackathons and knowledge conferences/webinars. Work with Principal Data/OR Scientists to devise solution roadmaps and KPIs.
Operational Responsibilities:
Design, develop and evaluate highly innovative DL/ML & Stats based models.
Establish scalable, efficient, and automated processes for DL/ML model development, model validation and model implementation.
Conduct feature engineering and implement DL/ML models into production by collaborating with software developers and machine learning engineers.
Develop processes and tools to monitor and analyse model performance and data accuracy.
Assess the effectiveness and accuracy of new data sources and data gathering techniques.
Aggregate data from multiple sources to provide a comprehensive assessment.
Create reports, presentations, and process documents to display impactful results.
Communicate solutions to stakeholders and implement improvements as needed to operational systems.
Recommend go / no-go on problem statements based on preliminary data analysis in consultation with Principal Data Scientists and AI Solution Managers
Stakeholder Management:
Collaborate with AI Champion and Business Data Analyst to define and ratify data requirements and sources.
Collaborate with AI Solution Managers, Principal Data Engineer, OR Scientist and Principal ML Engineer to build ML/DL models and data prototypes.
Experience:
2-5 years of relevant work experience
Educational Qualification:
PhD/MS/MTech in Computer Science/EE/Applied Mathematics/Optimization/other relevant disciplines involving computing and optimization.
Experience with Reinforcement Learning and GenAI.
Skills:
Mandatory
Knowledge of fundamentals in Probability, Statistics, Linear Algebra, Data Structures & Algorithms, Machine Learning & AI.
Performing Data exploration, pre-processing, Analysis, and evaluation Exploring and visualizing data to gain an Understanding of it and then identifying Differences in data distribution that could affect. Performance when deploying the model in the Real world.
Good coding skills preferably in Python, Spark, TensorFlow/ Py- Torch, Karas, etc. Familiarity with distributed computing and scalable systems preferred.
Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
Deep understanding of applied statistical analysis and predictive modelling is desired such as regression, SVM, Tree, Random Forests, Boosting, Neural Network, clustering, forecasting, pattern analysis.
Strong know-how of data exploration techniques, such as, mean-variance, k-means, nearest-neighbour, outlier techniques, and correlate anomalous sequences of events.
Extensive know-how of Python /R and in object-oriented programming languages with high code quality
Passionate about applying emerging DL/ML frameworks to solve business problems and like working in an extremely dynamic, fast paced environment.
Work with technology partners to translate your innovations into robust, scaled, analytical solutions; Communicate insights from, complex data or algorithms into simple conclusions that will empower others to act based on the insights you derive.
Proficiency with data mining, applied mathematics, and statistical analysis.
Preferred
Skilled in the use of business intelligence tools, such as Power BI, Tableau and frameworks like Hadoop, Hive etc.
Experience: 2+ years of experience in Python/ R/ Scala and Statistical Analysis, Machine learning Supervised and Unsupervised learning Techniques), neural networks, Deep Learning Techniques, Time-series analysis, NLP and chatbot systems, Reinforcement Learning, Model Optimization.
Working knowledge of software development languages
Have strong analytical skills and demonstrated proficiency in at least one programming language (R, Python, SQL, etc.) or family of technique (optimization, simulation, machine learning, predictive modelling, etc.)
Knowledge of Cloud Technologies are beneficial.
Visualization skills and ability to prepare advanced graphical presentations and storytelling.
Existing papers from CVPR, NIPS, ICML, KDD, and other key conferences are plus, but this is not a research position.
