Responsibilities
- Understand business requirements and analyze datasets to determine suitable approaches to meet analytic business needs and support data-driven decision-making
- Design and implement data analysis and ML models, hypotheses, algorithms and experiments to support data-driven decision-making
- Apply various analytics techniques like data mining, predictive modeling, prescriptive modeling, math, statistics, advanced analytics, machine learning models and algorithms, etc.; to analyze data and uncover meaningful patterns, relationships, and trends
- Design efficient data loading, data augmentation and data analysis techniques to enhance the accuracy and robustness of data science and machine learning models, including scalable models suitable for automation
- Research, study and stay updated in the domain of data science, machine learning, analytics tools and techniques etc.; and continuously identify avenues for enhancing analysis efficiency, accuracy and robustness
Qualifications
- Bachelor’s degree in computer science, Operational research, Statistics, Applied mathematics, or in any other engineering discipline.
- 2+ years of hands-on experience in Python programming for data analysis, machine learning, and with libraries such as NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, PyTorch, NLTK, spaCy, and Gensim.
- 2+ years of experience with both supervised and unsupervised machine learning techniques.
- 2+ years of experience with data analysis and visualization using Python packages such as Pandas, NumPy, Matplotlib, Seaborn, or data visualization tools like Dash or QlikSense.
- 1+ years' experience in SQL programming language and relational databases.
- 2+ years experience in any dashboarding tool such as Tableau, Power BI, DOMO and QlikSense
Preferred Qualifications
- An MS/PhD in Computer Science, Operational research, Statistics, Applied mathematics, or in any other engineering discipline. PhD strongly preferred.
- Experience working with Google Cloud Platform (GCP) services, leveraging its capabilities for ML model development and deployment.
- Experience with Git and GitHub for version control and collaboration.
- Besides Python, familiarity with one more additional programming language (e.g., C/C++/Java)
- Strong background and understanding of mathematical concepts relating to probabilistic models, conditional probability, numerical methods, linear algebra, neural network under the hood detail.
- Experience working with large language models such GPT-4, Google, Palm, Llama-2, etc.
- Excellent problem solving, communication, and data presentation skills.