*not applicable*qualification*:- *post-grad in one of the following fields with strong academic credentials*:- computer science/it.- operations research/applied math.- engineering.- statistics.*responsibility*:*business*:- '-works with the business team to identify the right business problem, gather the requirements and data required to answer the same.- data exploration, hypothesis testing and statistical modeling are part of daily activities.- involved in development, testing, evaluation and optimisation of models developed.- analyzes data and generates insights that can articulated to business stakeholders.- develops hypothesis for testing in consultation with principal/domain sme and business teams.*stakeholder management*:- '- poc for all the daily based activities and ensures the availabilty of all the required information with all the team at all the times.- '-build the collaterals which are durable and reusable -communicate analytical results in a way that is meaningful for business stakeholders and provides actionable insights.- coordinates in communicating the data needs with both technology and business teams to ensure that right data is captured for analysis and modeling.- 'design qualitative & quantitative research instruments & methods (example: machine learning models, surveys, interviews etc) to capture the data if required.- integrate qualitative & quantitative information to create insights.*project management*:- '-ensures that all the deliverables meets the delivery excellence standards and meets the stakeholders' expectations.- identifies risks to project execution and works with stakeholders to mitigate the same.- execute the design, analysis, or evaluation of assigned projects using sound engineering principles and adhering to business standards, practices, procedures, and product / program requirements.*data analytics and reporting*:- '- explore and examine data from multiple disparate sources.- prepare a data collection plan from both structured and unstructured sources.- collaborate and coordinate with technology and business teams for all data needs.- expert level proficiency in data handling (sql).*data discovery & profiling*:- '- perform exploratory data analysis and generate insights.- validate hypothesis developed during exploration phase.- present initial results to business stakeholders and identify the next steps.- design experiments with test and validate multiple hypothesis to meet/exceed expectations of customer due to the dynamic environment.*data modelling*:*create models using one or more of the platforms like r, sas, python, matlab model creation would involve one or more of the following technqiues*:- 1 classification.- 2 clusterning, segmentations.- 3 time series.- 4 market basket anaysis.- 5 text mining(structured and unstructured data).- 6 nlp, nlu, nlc.- 7 decision trees, rf.- 8 network analysis.- 9 linear programming.- 10 optimisation.- 11 deep learning.- '- testing and validating the model.- deriving insights and recommendations from the models.- performing data visualization and presentation to clients.*innovation & thought leadership*:- '- provide thoughtleadership and dependable execution on diverse projects.- implement best practices and technology.- discover new avenues by disecting the data and identify which all models can be utilised for a given business problem.- provide expertise thru pocs and povs.*knowledge management*:- 'prepare a design, requirement document.- document all modeling steps in a systematic way including modeling process, insights generated, presentations, model validation results and checklists built in the project.- prepare a one pager document that outlines and quantifies the business impact due to the ds project.*people/team management*:- ' mentor a team of data scientists.- set the timelines and monitor the progress of the project.- ensure the timely delivery of deliverables and addresses the concerns related to tasks.- understand aspirations of team members.- set goals for team members and monitor performance.- conduct appraisals.- identify, priorities and deploy action items for competency development.- guide the employee in setting career paths.*must have skills*- azure open ai service- aws machine learning- deep learning- python*good to have skills*- spark ml- statistics- transformer- eda(exploratory data analysis)- google vertex ai- d365 common data service- google cloud natural language- dialogflow virtual agents- dialogflow agent assist- ibm watson natural language- knowledge graph- tensorflow quantum- azure computer vision- ml ops- datarobot- rust- neuro ai- dataiku- machine learning- azure cognitive search- cloud automl- bigquery ml- automl tables- dialogflow- tensorflow serving- opencv- artificial intelligence- amazon sagemaker- databricks- iot- google dialogflow