About the job

Should have 10-15 years’ hands-on experience in algorithms and implementation of analytics solutions in predictive analytics, text analytics and image analytics

Expected to contribute to opportunity analysis, building project proposals, designing, implementation and execution across variety of ML projects in the areas of Predictive Modelling, Forecasting and Optimization

Should have 5-10 years’ of experience in leading a team of data scientists, works closely with client’s technical team to plan, develop and execute on client requirements providing technical expertise and project leadership. 

Manage data science work streams and eventually build a team of data scientists, ML experts and data engineers to solve business problems by applying advanced Machine Learning algorithms and complex statistical models on large volumes of data.

Work closely with product managers, software engineers, and infrastructure engineers to define strategy, roadmap and requirements

Experience with information retrieval, Natural Language Processing, Natural Language Understanding and Neural Language Modeling.

Ensure the data science team successfully delivers on design, development, testing, experimentation, and the operations of algorithms, data pipelines, and systems

Follow industry best practices, stay up to date with and extend the state of the art in machine learning research and practice, drive innovation by contributing towards publications and patents.

Regularly communicate with senior management on status, risk and strategy

Collaborate to develop analytics pipelines in production systems around continuous integration and learning.

Propose suitable technology stacks for projects to be deployed across cloud platforms and on-premises infrastructure.

Evaluates and leads broad range of forward looking analytics initiatives, track emerging data science trends, and knowledge sharing

Helps in new project proposals with advanced analytic architectures across functional areas as per requirement/opportunities.

Participate in internal technical councils and represent the organization in forums that involve community of data scientists across industry and academia.

Technical Role and Responsibilities

Demonstrated strong capability in statistical/Mathematical modelling or Machine Learning or Artificial Intelligence

Demonstrated skills in programming for implementation and deployment of algorithms preferably in Statistical/ML based programming languages in Python

Experience leading enterprise-wide data science projects from scratch to scale

Significant record of publications, exhibiting influence within an industry

Sound Experience with traditional as well as modern statistical techniques, including Regression, Support Vector Machines, Regularization, Boosting, Random Forests, and other Ensemble Methods

Highly proficient in Python, R, SQL, Spark, and other big data tools

Visualization tool experience - preferably with Tableau or Power BI

Sound knowledge of cloud ecosystems like Azure, AWS and Google cloud.

Provide technical recommendations and engage with ETL/BI Architects, Business SMEs and other stakeholders throughout the Solution/Data Architecture and implementation lifecycle and recommend effective solutions to develop high performance and highly scalable data solutions (data marts/warehouse and data mining and advanced analytics)

Experience in Big Data technologies like Hadoop, Spark, Hive, Pig, Presto, Cassandra, Kafka and NoSQL databases.

Data analysis, reporting, visualization expertise and experience with tools such as Tableau, Alteryx, Python and R in scale.

Carrying out statistical and mathematical modelling, solving complex business problems and delivering innovative solutions using state of the art tools and cutting-edge technologies for big data & beyond.

Good understanding about statistics – hypothesis testing, p-values, confidence intervals, regression, t-test, f-test and ANOVA

Domain experience in Storage, File systems, hybrid cloud environments is a plus.

A critical thinker that can quickly understand a new problem space and apply analytic techniques to identify potential value

Demonstrated thought leader with an interest in working on Digital Industrial transformation

Ability to evaluate quality of ML models and to define the right performance metrics for models in accordance with the requirements of core platform

Assist senior management in making key business decisions

Experience in interpreting and communicating analytic results to analytical and non-analytical business partners and executive decision makers in a lucid, precise, clear way

Strong knowledge on scalable ML model deployments with proficient experience on different tools like dockers, Azure, MLflow, Sagemaker and Kubernetes