Data Scientist will be applying statistical, advanced analytics, machine learning, and AI techniques to solve business problems. You will collaborate with a multi-disciplinary team of technical and non-technical business stakeholders on a wide range of challenges.


Excellent knowledge of ML algorithms (e.g., Linear Regression, Logistic Regression, Clustering/Segmentation, Decision Tree, Random Forest Algorithm, GBM, Naive Bayes, Support Vector Machines, Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) networks, Transformers, etc.)

Expertise in applying statistical, advanced analytics, machine learning, and/or AI, deep learning concepts, and techniques to solve business problems. Strong programming skills using Python, SQL, PySpark Expertise in one or more areas among Large Language Models (LLM), Transformers, Natural Language Processing (NLP), deep learning, recommender systems, and related fields is highly preferred.

Experience with Python libraries such as Pandas, NumPy, SciPy, Scikit-Learn

Strong analytical, critical-thinking skills with demonstrated ability to identify/analyze/synthesize data and use the data to drive decisions

Superior communication and persuasion skills with executives and non-technical stakeholders, talent for storytelling, visualization, and creating insights from data to deliver practical recommendations for business action

Good knowledge of calculus, linear algebra, statistics, and probability


  • Bachelor’s degree required, Master’s degree or Ph.D. preferred in Math, Statistics, Data Science, Analytics, Econometrics, Computer science, Operations Research, Behavioral Science, or another analytical/quantitative field required.
  • 5+ years of industry experience in data science, machine learning, AI, advanced analytics in a data scientist role, ML Scientist, research scientist, applied scientist or deep learning scientist role.
  • 4-5 years of experience with programming languages like Python, PySpark, SQL, and/or R.
  • Experience working with relational databases using SQL
  • Experience with data querying, wrangling, cleaning, and feature engineering working with common relational and non-relational databases in big data environments such as Azure, AWS, or Google Cloud
  • Experience generating data-driven insights and explaining machine learning and analytical concepts to diverse audiences and multiple levels of leadership
  • Experience with big data architecture and pipeline, such as Hadoop, Hive, Spark, and Kafka is a plus
  • Experience of ML Ops Technologies is mandatory.
  • Knowledge of AI Scenarios around ethics and governance.