Staff Data Scientist

Key Skills

Algorithms, Apache Hadoop, Apache Hive, Big Data Technology, C++, Data science techniques, Effective communication skills, Google Cloud Platform (GCP), Looker, Machine learning techniques, Natural Language Processing (NLP), NoSQL, Optimization, Python Programming, R Programming, Scala Programming, SPARK Programming, SQL, Statistics, Tableau, TensorFlow

Job Description

• Data Visualization: Requires knowledge of Visualization guidelines and best practices for complex data types; Multiple data visualization tools (for example, Python, R libraries, GGplot, Matplotlib, Ploty, Tableau, PowerBI etc.); Advanced visualization techniques/ tools; Multiple story plots and structures (OABCDE); Communication & influencing technique; Emotional intelligence. To generate appropriate graphical representations of data and model outcomes. Understand customer requirements to design appropriate data representation for multiple data sets. Work with User Experience designers and User Interface engineers as required to build front end applications. Present to and influence the team and business audience using the appropriate data visualization frameworks and conveys clear messages through business and stakeholder understanding. Customize communication style based on stakeholder under guidance, and leverages rational arguments. Guide and mentor junior associates on story types, structures, and techniques based on context.
• Understanding Business Context: Requires knowledge of Industry and environmental factors; Common business vernacular; Business practices across two or more domains such as product, finance, marketing, sales, technology, business systems, and human resources and in-depth knowledge of related practices; Directly relevant business metrics and business areas. To Provide recommendations to business stakeholders to solve complex business issues. Develop business cases for projects with a projected return on investment or cost savings. Translate business requirements into projects, activities, and tasks and aligns to overall business strategy and develops domain specific artifact. Serve as an interpreter and conduit to connect business needs with tangible solutions and results. Identify and recommend relevant business insights pertaining to their area of work.
• Tech. Problem Formulation: Requires knowledge of Analytics/big data analytics / automation techniques and methods; Business understanding; Precedence and use cases; Business requirements and insights. To translate/ co-own business problems within one's discipline to data related or mathematical solutions. Identify appropriate methods/tools to be leveraged to provide a solution for the problem. Share use cases and gives examples to demonstrate how the method would solve the business problem.
• Analytical Modeling: Requires knowledge of feature relevance and selection; Exploratory data analysis methods and techniques; Advanced statistical methods and best-practice advanced modelling techniques (e.g., graphical models, Bayesian inference, basic level of NLP, Vision, neural networks, SVM, Random Forest etc.); Multivariate calculus; Statistical models behind standard ML models; Advanced excel techniques and Programming languages like R/Python; Basic classical optimization techniques (e.g., Newton-Rapson methods, Gradient descent); Numerical methods of optimization (e.g. Linear Programming, Integer Programming, Quadratic Programming, etc.) To select appropriate modeling techniques for complex problems with large scale, multiple structured and unstructured data sets. Select and develop variables and features iteratively based on model responses in collaboration with the business. Conducts exploratory data analysis activities (for example, basic statistical analysis, hypothesis testing, statistical inferences) on available data. Identify dimensions and designs of

• experiments and create test and learn frameworks. Interpret data to identify trends to go across future data sets. Create continuous, online model learning along with iterative model enhancements. Develop newer techniques (for example, advanced machine learning algorithms, auto ML) by leveraging the latest trends in machine learning, artificial intelligence to train algorithms to apply models to new data sets. Guide the team on feature engineering, experimentation, and advanced modeling techniques to be used for complex problems with unstructured and multiple data sets (for example, streaming data, raw text data).
• Model Deployment and Scaling: Requires knowledge of impact of variables and features on model performance; understanding of servers, model formats to store models. To deploy models to production. Continuously log and track model behavior once it is deployed against the defined metrics. Identify model parameters which may need modifications depending on scale of deployment.
• Code Development and Testing: Requires knowledge of coding languages like SQL, Java, C++, Python and others; Testing methods such as static, dynamic, software composition analysis, manual penetration testing and others; Business, domain understanding. To write code to develop the and cost- effectiveness; and participating in and supporting community outreach events.

You’ll sweep us off our feet if… 

• Well balanced skills – People have trouble pinning you down. Both business and technical/ data teams claim you as their own. You are equal parts technical and functional. You are equally comfortable and effective in persuading both technical and business audience
• Great communicator – You’re capable of telling the story behind the data and resulting insights to exec audience and you are a pro at building slide decks that can simplify any complex story. You can customize your communication style based on stakeholders. You can guide and coach junior associates on story types, structures, and techniques based on context.
• You are able to anticipate the needs of business stakeholders in the relevant business context.
• You can collect, organize, analyze, and disseminate significant amounts of information with attention to detail and accuracy. You are equally strong at getting into the weeds and making sure that the data needed to support these metrics is consistent and available. You are very comfortable rolling up your sleeves when needed and writing complex codes for quick prototyping and helping junior associates debug their work and raise the bar. 
• You are a pro at leveraging visualization to build compelling stories based on context to integrate multiple pieces of information into cohesive insights.

Minimum Qualifications: 

• Option 1: Bachelors degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field and 4 years' experience in an analytics related field. Option 2: Masters degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field and 2 years' experience in an analytics related field.

• Advanced proficiency in SQL, Python, Spark, Scala, or R
• 3+ years’ experience using open-source learning frameworks (for example, Scikit Learn, TensorFlow, Torch)
• Working knowledge and experience with supervised models (GLM, classification models, ensemble techniques, regularization techniques, Bayesian models, tree-based models and Neural Networks), and unsupervised models (e.g., K-means clustering, Probabilistic Clustering, Principal Component Analysis, etc.)

Preferred Qualifications:

• PhD in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics, or related field.
• 7 years' experience in data science, machine learning, optimization models, or related field. Successful completion of one or more assessments in Python, Spark, Scala, or R.
• 5 years’ experience using open source frameworks (for example, scikit learn, tensorflow, torch).
• Hands-on experience developing and deploying advanced & scalable modeling initiatives, ideally within the marketing domain.
• Knowledge of marketing strategies, advertising operations, and marketing attribution methods.
• Experience leveraging cloud based big data technologies (Hive/Hadoop) and modern data visualization tools (Tableau, ThoughtSpot, Looker) to blend data from multiple sources to help answer multi-dimensional business questions.
• Experience transforming/automating data within cloud architectures (ie. Google Cloud Platform).
• Experience with various types of customer data.
• You have built machine learning models to solve end-to-end business problems using time series, ensemble methods, deep learning etc.