Data Scientist

JOB PURPOSE

• The Data Scientist is responsible for modeling business processes and problems and for discovering actionable insights through descriptive, predictive, and prescriptive analytics.
• The Data Scientist will use Statistical, Machine Learning, Deep Learning, Data Visualization, and other analytics and AI techniques to gain understanding of the business processes and problems, and develop analytics solutions.
• The Data Scientist will contribute to building and developing data infrastructure for IDO and its portfolio companies and will support data exploration, preparation, collection, integration, and operationalization of data architectures and pipelines.
• The Data Scientist will be a data and analytics evangelist and an expert and will promote the use of data and analytics capabilities and benefits to leaders of IDO portfolio companies and educate them in leveraging these capabilities in achieving their business goals.
• The Data Scientist will support the leadership with insights gained from data analyses and analytics, management reports, and analyses for decision-making processes.

CORE RESPONSIBILITIES:

Program Analysis:

• Understand the decision-making process, workflows, and business and information needs of the users in IDO portfolio companies.
• Translate business needs into analytics requirements to support decision processes and workflows with required information.
• Work with the users to identify data-driven ML/AI/BI business opportunities and investigate solutions.
• Prioritize, scope, and manage Data Science projects and develop the corresponding KPIs to ensure project tracking and progress.

Data Integration and Exploratory Data Analysis:

• Develop access to databases and other data sources for exploratory data analysis.
• Work with domain experts to understand the business mechanics that generate the data.
• Identify data pipelines for efficient and repeatable data science projects that may span multiple divisions within a company or multiple companies in IDO.
• Use data analysis and visualization techniques for studying data sets and developing insights while working with the business users.
• Generate hypotheses about the underlying mechanics of the business process and test the hypotheses using quantitative methods.
• Perform large-scale data exploration to identify hidden or unknown relationships between variables in datasets, and validate or invalidate the new or existing hypotheses.

Analytics:

• Implement ML and other AI techniques to perform regression, classification, prediction, etc. as appropriate. This includes setting up, trialing, and testing hypotheses till a solution is identified while ensuring that the domain knowledge is effectively used and the business users are involved.
• Perform model testing in a structured manner ensuring validation of biases/fairness in the model. Research and implement state-of-the-art techniques and tools in machine learning, deep learning, and artificial intelligence to ensure that systems created are efficient and effective.
• Ensure that the data sources have sufficient data while selecting a model for production.
• Determine and ensure availability and feasibility of data and data infrastructure requirements that will be needed to train, evolve, and operationalize models and algorithms.
• Visualize information and develop reports on the results of data analysis using data visualization tools and develop dashboards where a BI/descriptive analytics solution is appropriate.

Substantive Expertise:

• Be ready to continue to change course if hypotheses during model development are not supported by data analysis while keeping the objectives of the initiative in perspective.
• Make sure that common biases including confirmation bias, loss aversion, and anchoring bias, are kept in check during model selection and development.
• Use judgement to form conclusions that may challenge existing and conventional judgement and established ideas and thought, and focus on the goals of the initiative to identify high-leverage intervention points and strategies.
• Seek to understand business needs and get results that have a clear, positive, and direct impact on business performance.
• Apply multiple strategies including social and data-driven methods to convince others to change their opinions or plans and ensure that proposals or arguments are supported by effective logic and a business case while relevant factors are comprehensively addressed.
• Consider the relative costs and benefits of potential actions to choose the most appropriate one for selection, and operationalization of the proposed model.
• Be ready to learn, re-learn, and unlearn the problems while working simultaneously on multiple business units and portfolio companies.
• Rapidly acquire new knowledge and learn new skills as needed.

Data Pipeline Operationalization:

• Work with Data Engineering and IT to evaluate, select, and implement analytics deployment.
• Develop and help integrate model performance assessment and validation tools, and continuous monitoring in the deployed solution.
• Collaborate with Data Engineering to establish best practices for analytics production pipelines.

Miscellaneous:

• Train peers in IDO and IDO portfolio companies on Data Science principles and techniques.
• Help inspire the organizations about the business potential of Artificial Intelligence and other Data Science techniques.
• Develop network of Data Science enthusiasts and professionals in the IDO Universe.
• Keep abreast of evolving tools, technologies, and skills through self-learning, conferences, publications, courses, local academia and meetups.

COMMUNICATION & WORKING RELATIONSHIPS:

Internal:

• Data Engineering Team
• Data Governance Team

Reasons for Interaction:

• To collaborate on projects
• To ensure compliance

External:

• With portfolio companies in IDO Universe

Reasons for Interaction:

• For working on Analytics Projects

QUALIFICATIONS, EXPERIENCE, & SKILLS:

Educational and Professional Qualifications:

• A Master’s degree in Computer Science, Engineering, Data Science, Operations Research, Statistics, Applied Mathematics, or a related field. Education in equivalent areas when complemented by suitable experience will be considered.
• A specialization in ML, AI, Analytics, or Data Science is a plus.
• A doctorate is a plus.
• Analytics experience in multiple domains is a plus.

Skills and Experiences:

• Computing and IT:
• Strong expertise in at least two programming languages preferably Python and Java.
• Experience in SQL for relational databases and in writing complex queries.
• Experience with distributed data storage/computing tools such as Apache Spark or MapReduce, and Hadoop.
• Experience of working across multiple deployment environments and multiple operating systems.
• Experience in or exposure to distributed streaming platform such as Kafka.
• Experience in or exposure to container tools such as Docker or Kubernetes.
• Data Science:
• Experience in one or more of the commercial/open-source exploratory data analysis tools (e.g., Python in Jupyter environment with Pandas, SciPy, Numpy, Matplotlib, etc.)
• Experience in web-scraping tools such as BeautifulSoup, Scrapy, and Urlib.
• Experience in ML libraries such as TensorFlow, PyTorch, Spark Mlib, Scikit-Learn, Keras, and MXNet, etc.
• Experience in using Cloud-based analytics platforms such as IBM Watson, Amazon SageMaker or Comprehend, or the Google AI Platform.
• Experience in using a visualization tool such as Power BI or Tableau.
• Experience in solving business problems using Machine Learning, Deep Learning, and other analytical methods for regression, classification, clustering, text processing, and collaborative filtering, etc.
• Expertise in state-of-the-art Machine/Deep Learning and Data Analytics tools and techniques.
• Experience in using both structured and unstructured data sources including text and documents.
• Experience in developing or supporting development of production-level data pipelines.
• Experience in productionizing analytics solutions and supporting their operationalization.
• Miscellaneous:
• Adept in agile methodologies and well-versed in applying them to analytics projects through DevOps and DataOps.
• End-to-end experience in applying ML and Analytics to business problems including problem analysis, exploratory data analysis, model selection and development, and supporting operationalization.
• Experience in launching and leading significant data science projects.
• Personal Characteristics:
• Willingness and ability to learn new technologies while on the job fulfilling normal duties.
• Ability to communicate complex results to technical and non-technical audiences.
• Self-driven, collaborative, curious and creative.
• Ability to work in diverse, cross-functional teams in a dynamic business environment.
• Confident, energetic self-starter, with strong moderation and communication skills.
• Superior presentation skills, including storytelling and other techniques to guide and inspire.

Years of Experience:

The candidate should have 4 to 6 years of relevant experience.