ML engineer

Factspan Overview:
Factspan is a pure play data and analytics services organization. We partner with fortune 500 enterprises to build an analytics centre of excellence, generating insights and solutions from raw data to solve business challenges, make strategic recommendations and implement new processes that help them succeed. With offices in Seattle, Washington and Bangalore, India; we use a global delivery model to service our customers. Our customers include industry leaders from Retail, Financial Services, Hospitality, and technology sectors.

Responsibilities

➢ Selecting features, building, and optimizing classifiers/regression using machine
learning and deep learning techniques
➢ Proficient in using data analytics tools to perform queries and analyses and for defining
and correlating data, and skilled at utilizing data visualization platforms to organize
and present summarizations, predictive analysis, comparative analysis, dashboards, and
reporting.
➢ Processing, cleansing, and verifying the integrity of data used for analysis.
➢ Performing data mining and analytics to support ongoing continuous risk monitoring
and risk assessments of operational data to recognize patterns and trends, investigate
anomalies, and assess internal control environment.
➢ Utilize data analysis by leveraging various statistical techniques, and predictive
modeling to drive and identify indicators of risk
➢ Drive efficiency by automation of manual processes

More responsibilities in detail:
➢ Excellent understanding of machine learning algorithms, such as Random Forest,
Gradient Boosting, Naive Bayes, SVM, KNN. Good understanding of deep learning
algorithms, such as DNN, CNN, RNN, LSTM, Autoencoders.
➢ Deep Knowledge of ML/AI software and packages such as python: scikit-learn,
TensorFlow and R: CARET, PyTorch.
➢ Proficiency in statistics concepts: sampling theory, descriptive statistics, probability
distributions, statistical tests, dimensionality, reduction, Hypothesis testing, maximum
likelihood estimators, inference, etc.
➢ Expertise in model validation, hyperparameter tuning, and model selection techniques
such as cross validation, leave-one-out, bootstrap.
➢ Proficiency in using query languages such as SQL and spark.
➢ Services, Reporting Service, Power BI, Python, PySpark- Distributed Computing. Machine
Learning, Times Series, Data Mining, Mathematical, Modeling, Probability and Stochastic
Processes