Data Scientist
3 weeks ago
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.
Responsibilities:
- 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.
Requirements:
- 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.
- 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.
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