Data Scientist

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Location: Kolkata (India)

Job Description:

Primary Responsibilities:

  • Data Exploration and Preparation:
    • Apply statistical analysis and visualization techniques to various data, such as hierarchical clustering, T-distributed Stochastic Neighbor Embedding (t-SNE), principal components analysis (PCA)
    • Generate hypotheses about the underlying mechanics of the business process
    • Test hypotheses using various quantitative methods
    • Display drive and curiosity to understand the business process to its core
    • Network with domain experts to better understand the business mechanics that generated the data
  • Machine Learning:
    • Apply various ML and advanced analytics techniques to perform classification or prediction tasks
    • Integrate domain knowledge into the ML solution; for example, from an understanding of financial risk, customer journey, quality prediction, sales, marketing
    • Testing of ML models, such as cross-validation, A/B testing, bias and fairness
  • Operationalization:
    • Collaborate with ML operations (MLOps), data engineers, and IT to evaluate and implement ML deployment options
    • (Help to) integrate model performance management tools into the current business infrastructure
    • (Help to) implement champion/challenger test (A/B tests) on production systems
    • Continuously monitor execution and health of production ML models
    • Establish best practices around ML production infrastructure

Education and Training:

  • A bachelor’s or master’s degree in computer science, data science, operations research, statistics, applied mathematics, or a related quantitative field is required. Alternate experience and education in equivalent areas such as economics, engineering or physics, is acceptable. Experience in more than one area is strongly preferred.
  • Candidates must have a specialization in ML, AI, cognitive science or data science.

Previous Experience:

  • Candidates should have three to six of relevant project experience in successfully executing data science projects. Preferably in the domains of risk modeling, customer behavior prediction, quality assessment.
  • A specialization in text analytics or other specialized ML techniques such as deep learning, etc., is required.
  • Ideally, the candidates are adept in agile methodologies and well-versed in applying DevOps/MLOps methods to the construction of ML and data science pipelines.
  • Candidates should exhibit significant project experience in applying ML and data science to business functions such as financial risk analytics, logistics, marketing analytics, quality assessment, e-commerce platforms, process control, target marketing, churn management, etc.
  • Candidates need to demonstrate that they were instrumental in launching significant data science projects.

IT Knowledge/Skills:

  • Expert Coding knowledge and experience in several languages: for example, R, Python/Jupyter, Java, Scala, etc.
  • Experience with popular database programming languages including SQL, and others for relational databases and upcoming nonrelational databases such as NoSQL/Hadoop-oriented databases such as HBase, Cassandra, others.
  • Experience with distributed data/computing tools: MapReduce, Hadoop, Hive, Kafka, others
  • Experience of working across multiple deployment environments including cloud, on-premises and hybrid, multiple operating systems and through containerization techniques such as Docker, Kubernetes, and others

Machine Learning and Data Science Knowledge/Skills:

  • Expert Experience in one or more of the following commercial/open-source data discovery/analysis platforms: RStudio, Spark, KNIME, Microsoft AzureML.
  • Expertise in solving text analytics, credit scoring, failure prediction problems is preferable.
  • Knowledge and experience in statistical and data mining techniques: generalized linear model (GLM)/regression, random forest, boosting, trees, text mining, hierarchical clustering, deep learning, convolutional neural network (CNN), recurrent neural network (RNN), T-distributed Stochastic Neighbor Embedding (t-SNE), graph analysis, etc.

Interpersonal Skills and Characteristics:

  • All candidates must be self-driven, curious and creative.
  • They must demonstrate the ability to work in diverse, cross-functional teams in a dynamic business environment.
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