Master s degree in a quantitative discipline, e.g., Computer Science, Mathematics, Statistics, Artificial Intelligence.
Hands-on experience with statistical software tools. We prefer experience in Python. and Python statistical libraries. Experience in R is also accepted, as is SAS, SPSS, Strata, and MATLAB.
High proficiency with standard database skills (e.g., SQL), data preparation, cleaning, and wrangling/munging.
Deep conceptual understanding of probability & statistics, ML algorithm intuition, and computer science fundamentals.
Deep experience in statistical and machine learning techniques such as classification, regression, feature selection and feature engineering, hyperparameter tuning, unsupervised learning methods, etc.
Preferred Experience:
An analytical mind with problem-solving abilities
Conceptualize and develop relevant NLP & Text Mining solutions.
Extract, combine and clean data from several sources for model building.
Experience with NoSQL databases (Mongo, Cosmos, etc.)
Knowledge and ability to use text mining libraries in Python such as NLTK, Text Blob, Spacy, etc.
Understanding of text representation techniques (BERT, ELMo, etc.) and statistics
Deep understanding of the LSTM/CNN functionality and architecture
Experience with information extraction and retrieval techniques (e.g. Named Entity Recognition, Dependency Parsing, Coreference Resolution, etc.)
Ability to apply ML/DL libraries and frameworks to apply in NLP tasks
Experience with any cloud DS/AI platforms (e.g. AWS Sage maker, Azure Machine Learning, etc.) will be a bonus.
Experience with fundamental building blocks of AI, such as natural language processing and
computer vision.
Experience with less common techniques, such as probabilistic graphical models, generative algorithms, genetic algorithms, reinforcement learning, etc.
Personal projects and Kaggle competition results can serve as differentiation.
Responsibilities:
Research machine learning algorithms develop solution formulations, and test on large datasets.
Given unstructured and complex business problems, design and develop tailored analytic solutions.
Design experiments, test hypotheses, and build actionable models.
Solve analytical problems, and effectively communicate methodologies and results.
Draw relevant inferences and insights from data including identification of trends and anomalies.
Translate unstructured, complex business problems into abstract mathematical frameworks, making intelligent analogies and approximations to produce working algorithms at scale.
Required Soft Skills:
Strong interpersonal and communication skills.
Ability to explain statistical reasoning to both experts and non-experts.
Strong communication and interpersonal skills.
Ability to learn new skills/technologies quickly and independently.
