Responsibilities
1. Understanding requirements, designing a solution using on AI/Gen AI technologies to meet the requirements
2. Data mining or extracting usable data from valuable data sources
3. Carrying out preprocessing of structured and unstructured data
4. Enhancing data collection procedures to include all relevant information for developing analytic systems
5. Processing, cleansing, and validating the integrity of data to be used for analysis
6. Analyzing large amounts of information to find patterns and solutions
7. Using machine learning tools to select features, create and optimize classifiers
8. Developing prediction systems and machine learning algorithms
9. Continuously refine and optimize models for performance, scalability, and efficiency.
10. Deploy models into production environments and monitor their performance
11. Explore and experiment with generative AI models (text and image generation models) suitable for the given requirements.
12. Train and evaluate generative models, fine-tuning parameters for desired outputs.
13. Integrate generative models into production workflows or applications
14. Work with senior data scientists to implement solutions for business problems
Requirements
1. Experience: 3-4 years of proven experience in data science, AI/ML field.
2. Education: Bachelor’s degree/ master’s degree in computer science, Statistics, Applied Math, or related field.
3. Programming Skills: Mastery in Python and database query languages like SQL.
4. Familiarity with R, Java, C++ is an added advantage.
5. Statistics: Good applied statistical skills, including knowledge of statistical tests, distributions, regression, maximum likelihood estimators, etc.
6. Machine Learning: good knowledge of machine learning methods like k-Nearest Neighbors, Naive Bayes, SVM, Decision Tree, time series predictions, Deep neural networks, recommendation systems, collaborative filtering etc.
7. Generative AI: Good experience of Gen AI frameworks and components like Langchain, vector databases, text/image generation models from Huggingface, Ollama, Open AI etc.
8. Deep learning/machine learning tools: Hands on experience with data science tools such as TensorFlow, Keras, Scikit-learn, PyTorch
9. Strong Math Skills (Multivariable Calculus and Linear Algebra): understanding the fundamentals of Multivariable Calculus and Linear Algebra is important as they form the basis of a lot of predictive performance or algorithm optimization techniques.
10. Data Wrangling: proficiency in handling imperfections in data is an important aspect of a data scientist job description.
11. Experience with Data Visualization Tools like matplotlib, seaborn, ggplot, d3.js., Power BI, Tableau that help to visually encode data
12. Excellent Communication Skills:
13. Strong Software Engineering Background
14. Problem-solving aptitude
15. Analytical mind and great business sense
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