Data Scientist

Job Description

As a Data Scientist, you will be part of a high performing team working on exciting opportunities in AI/ML within Ford Credit. We are looking for a seasoned Data Scientist with proven expertise in implementing Machine Learning/Optimization solutions with familiarity in Generative AI and a good grasp of Statistics. Develop Machine Learning (Supervised/Unsupervised learning), Neural Networks (ANN, CNN, RNN, LSTM, Decision tree, Encoder, Decoder), Natural Language Processing, Generative AI (LLMs, Lang Chain, RAG, Vector Database) . Hands-on Expertise in Python programming (OOPs concepts), SQL (relational/non-relational databases), experience in handling various data science libraries (Pandas, NumPy, SciPy, Sklearn, TensorFlow, Keras, Pytorch, etc. ) would be a necessary requirement. Exposure to Cloud technologies (e. g. , Google Cloud/AWS/Azure), including executing Machine Learning algorithms on Cloud is necessary. Exposure to Generative AI technologies. Professional Qualification: Potential candidates should possess 3 to 7 years of working experience as a Data Scientist. BE/MSc/ MTech /ME/PhD (Computer Science/Maths, Statistics). Possess a strong analytical mindset and be very comfortable with data. Experience with handling both relational and non-relational data. Hands-on with analytics methods (descriptive / predictive / prescriptive) , Statistical Analysis, Probability and Data Visualization tools (Python-Matplotlib, Seaborn). Background of Computer Science with excellent Data Science working experience. Technical Experience: Develop Machine Learning (Supervised/Unsupervised learning), Neural Networks (ANN, CNN, RNN, LSTM, Decision tree, Encoder, Decoder), Natural Language Processing, Generative AI (LLMs, Lang Chain, RAG, Vector Database) . Hands-on Expertise in Python programming (OOPs concepts), SQL (relational/non-relational databases), experience in handling various data science libraries (Pandas, NumPy, SciPy, Sklearn, TensorFlow, Keras, Pytorch, etc. ) would be a necessary requirement. Exposure to Cloud technologies (e. g. , Google Cloud/AWS/Azure), including executing Machine Learning algorithms on Cloud is necessary. Exposure to Generative AI technologies. Ability to scope the problem statement, data preparation, training and making the AI/ML model production ready. Work with business partners to understand the problem statement, translate the same into analytical problem. Ability to manipulate structured and unstructured data. Develop, test and improve existing machine learning models. Analyse large and complex data sets to derive valuable insights. Research and implement best practices to enhance existing machine learning infrastructure. Develop prototypes for future exploration. Design and evaluate approaches for handling large volume of real data streams. Ability to determine appropriate analytical methods to be used. Collaborate with data engineers, solutions architects, application engineers, and product teams across time zones to develop data and model pipelines