Your Opportunity:

As a Data Scientist for Walmart Labs, you’ll have the opportunity to

• Drive data-derived insights across the wide range of retail divisions by developing advanced statistical models, machine learning algorithms and computational algorithms based on business initiatives
• Direct the gathering of data, assessing data validity and synthesizing data into large analytics datasets to support project goals
• Utilize big data analytics and advanced data science techniques to identify trends, patterns, and discrepancies in data. Determine additional data needed to support insights
• Build and train statistical models and machine learning algorithms for replication for future projects
• Communicate recommendations to business partners and influencing future plans based on insights

Your Responsibility:

• Very good knowledge of the foundations of machine learning and statistics
• Experience in analyzing the complex problems and translating them into data science problem
• Experience in machine learning, supervised and unsupervised and deep learning.
• Hands on experience in Computer Visions and NLP.
• Experience with big data analytics – identifying trends, patterns, and outliers in large volumes of data
• Strong Experience in Python with excellent knowledge of Data Structures
• Strong Experience with big data platforms – Hadoop (Hive, Pig, Map Reduce, HQL, Scala, Spark)
• Hands on experience with Git
• Experience with SQL and relational databases, data warehouse

Your Qualifications:

• Bachelors with > 5 years of experience / Master’s degree with > 3 years of experience. Educational qualifications should be preferably in Computer Science/Mathematics/Statistics or a related area. Experience should be relevant to the role
• Must have:
• Good knowledge of Probability and Statistics, Linear Algebra, Convex Optimization, Algorithms
• Good knowledge of Machine Learning fundamentals (Supervised learning, Unsupervised learning, Regression, Classification, Clustering, Maximum Likelihood Estimation, Regularization, …)
• Good knowledge of ML models like Logistic Regression, SVMs, Random Forests, Gaussian Mixture Models among others
• Good knowledge of Deep Learning models and fundamentals like CNN, LSTM and Sequence Models, Word Embeddings (Word2Vec or Glove, BERT,…)
• Good knowledge of a Deep Learning framework- Tensorflow or PyTorch
• Good knowledge of Python including numpy, sklearn, pandas
• A very strong problem-solving mindset, with eagerness to learn (and this can compensate for weakness in any of the above points)