To support building data and analytics practice assets and support go-to-market strategy on data and analytics developments in the financial services market.
The functional coverage of this consulting practice extends across financial services’ risk and regulatory frameworks, predictive modelling, finance processes, risk transformation, risk and regulatory remediation, optimizing risk and regulation implementation and ongoing adherence and delivering end to end solutions required to enable insights through analytics.
Skills and qualifications:
- Good understanding of hypothesis testing and of statistical tests for significance.
- Deep hands on handling unstructured data such as digital footprints, keywords, browsing history and behavior and applied on model development
- Understanding of univariate, multivariate & time series analysis.
- Understanding of Bayesian and probabilistic models.
- Hand-on in machine learning supervised techniques like regression, classification, decision trees, ensemble algorithms (boosted trees) and random forests etc.
- Good understanding of unsupervised algorithms clustering, dimensionality reduction, Boltzmann machines (GBM), RBM etc.
- Some knowledge of Deep neural networks like ANN, CNN, LSTM’s, Seq2Seq, GAN’s, Word Vectors etc.
- Knowledge of Tensorflow, Pytorch, Keras etc.
- Nice to have some basic understanding of reinforcement learning like DQN, A3C etc.
- Deep hands on experience in one or more of these data science toolkits, such as R, Python, Spark, Weka, NumPy, MatLab, etc.
- Experience with data visualization tools, such as D3.js, GGplot, Tableau, Qlikview etc.
- Proficiency in using query languages such as SQL, Hive etc.
- Good to have some experience with NoSQL databases
- Some basic understanding of handling unstructured data in form of text, speech, pictures and video’s