Decision Science Analyst

Responsibilities:

• Design and develop strong analytics system and predictive models
• Managing a team of data scientists, machine learning engineers, and big data specialists
• Identify valuable data sources and automate data collection processes
• Undertake pre-processing of structured and unstructured data
• Analyze large amounts of information to discover trends and patterns
• Build predictive models and machine-learning algorithms
• Combine models through ensemble modeling
• Present information using data visualization techniques
• Propose solutions and strategies to business challenges
• Collaborate with engineering and product development teams

Requirements:

• Proven experience as a seasoned Data Scientist
• Good Experience in data mining processes
• Understanding of machine learning and Knowledge of operations research is a value addition
• Strong understanding and experience in R, SQL, and Python; Knowledge base with Scala, Java, or C++ is an asset
• Experience using business intelligence tools (e. g. Tableau) and data frameworks (e. g. Hadoop)
• Strong math skills (e. g. statistics, algebra)
• Problem-solving aptitude
• Excellent communication and presentation skills
• Experience in Natural Language Processing (NLP)
• Strong competitive coding skills
• BSc/BA in Computer Science, Engineering or relevant field; graduate degree in Data Science or other quantitative field is preferred

• Proficiency in shell scripting
• Proficiency in automation of tasks
• Proficiency in Pyspark/Python
• Proficiency in writing and understanding of sqoop
• Understanding of CloudEra manager
• Good understanding of RDBMS
• Good understanding of Excel

What you’ll do

• Deliver plugins for our Python-based ETL pipelines.
• Deliver Python microservices for provisioning and managing cloud infrastructure.
• Implement algorithms to analyse large data sets.
• Draft design documents that translate requirements into code.
• Deal with challenges associated with handling large volumes of data.
• Assume responsibilities from technical design through technical client support.
• Manage expectations with internal stakeholders and context-switch in a fast paced environment.
• Thrive in an environment that uses AWS and Elasticsearch extensively.
• Keep abreast of technology and contribute to the engineering strategy.
• Champion best development practices and provide mentorship.

What we’re looking for

• Experience in Python 3.
• Python libraries used for data (such as pandas, numpy).
• AWS.
• Elasticsearch.
• Performance tuning.
• Object Oriented Design and Modelling.
• Delivering complex software, ideally in a FinTech setting.
• CI/CD tools.
• Knowledge of design patterns.
• Sharp analytical and problem-solving skills.
• Strong sense of ownership.
• Demonstrable desire to learn and grow.
• Excellent written and oral communication skills.
• Mature collaboration and mentoring abilities.

About SteelEye Culture

• Work from home until you are vaccinated against COVID-19
• Top of the line health insurance • Order discounted meals every day from a dedicated portal
• Fair and simple salary structure
• 30+ holidays in a year
• Fresh fruits every day
• Centrally located. 5 mins to the nearest metro station (MG Road)
• Measured on output and not input

Director - Applied AI

Who we are?

Searce is  a niche’ Cloud Consulting business with futuristic tech DNA. We do new-age tech to realise the “Next” in the “Now” for our Clients. We specialise in Cloud Data Engineering, AI/Machine Learning and Advanced Cloud infra tech such as Anthos and Kubernetes. We are one of the top & the fastest growing partners for Google Cloud and AWS globally with over 2,500 clients successfully moved to cloud.

What do we believe?

• Best practices are overrated

• Implementing best practices can only make one an average .

• Honesty and Transparency

• We believe in naked truth. We do what we tell and tell what we do.

• Client Partnership

• Client - Vendor relationship: No. We partner with clients instead. 
• And our sales team comprises 100% of our clients.

How do we work ?

It’s all about being Happier first. And rest follows. Searce work culture is defined by HAPPIER.

• Humble: Happy people don’t carry ego around. We listen to understand; not to respond.

• Adaptable: We are comfortable with uncertainty. And we accept changes well. As that’s what life's about.

• Positive: We are super positive about work & life in general. We love to forget and forgive. We don’t hold grudges. We don’t have time or adequate space for it.

• Passionate: We are as passionate about the great street-food vendor across the street as about Tesla’s new model and so on. Passion is what drives us to work and makes us deliver the quality we deliver.

• Innovative: Innovate or Die. We love to challenge the status quo.

• Experimental: We encourage curiosity & making mistakes.

• Responsible: Driven. Self motivated. Self governing teams. We own it.

So, what are we hunting for ?

1. To devise strategy through the delivery of sustainable intelligent solutions, strategic customer engagements, and research and development
2. To enable and lead our data and analytics team and develop machine learning and AI paths across strategic programs, solution implementation, and customer relationships
3. To manage existing customers and realize new opportunities and capabilities of growth
4. To collaborate with different stakeholders for delivering automated, high availability and secure solutions
5. To develop talent and skills to create a high performance team that delivers superior products
6. To communicate effectively across the organization to ensure that the team is completely aligned to business objectives
7. To build strong interpersonal relationships with peers and other key stakeholders that will contribute to your team's success

Your bucket of Undertakings :

1. Develop an AI roadmap aligned to client needs and vision
2. Develop a Go-To-Market strategy of AI solutions for customers
3. Build a diverse cross-functional team to identify and prioritize key areas of the business across AI, NLP and other cognitive solutions that will drive significant business benefit
4. Lead AI R&D initiatives to include prototypes and minimum viable products
5. Work closely with multiple teams on projects like Visual quality inspection, ML Ops, Conversational banking, Demand forecasting, Anomaly detection etc. 
6. Build reusable and scalable solutions for use across the customer base
7. Create AI white papers and enable strategic partnerships with industry leaders
8. Align, mentor, and manage, team(s) around strategic initiatives
9. Prototype and demonstrate AI related products and solutions for customers
10. Establish processes, operations, measurement, and controls for end-to-end life-cycle management of the digital workforce (intelligent systems)
11. Lead AI tech challenges and proposals with team members
12. Assist business development teams in the expansion and enhancement of a pipeline to support short- and long-range growth plans
13. Identify new business opportunities and prioritize pursuits for AI 

Education & Experience : 

1. Advanced or basic degree (PhD with few years experience, or MS / BS (with many years experience)) in a quantitative field such as CS, EE, Information sciences, Statistics, Mathematics, Economics, Operations Research, or related, with focus on applied and foundational Machine Learning , AI , NLP and/or / data-driven statistical analysis & modelling
2. 10+ years of Experience majorly in applying AI/ML/ NLP / deep learning / data-driven statistical analysis & modelling solutions to multiple domains, including financial engineering, financial processes a plus
3. Strong, proven programming skills and with machine learning and deep learning and Big data frameworks including TensorFlow, Caffe, Spark, Hadoop. Experience with writing complex programs and implementing custom algorithms in these and other environments
4. Experience beyond using open source tools as-is, and writing custom code on top of, or in addition to, existing open source frameworks
5. Proven capability in demonstrating successful advanced technology solutions (either prototypes, POCs, well-cited research publications, and/or products) using ML/AI/NLP/data science in one or more domains
6. Experience in data management, data analytics middleware, platforms and infrastructure, cloud and fog computing is a plus
7. Excellent communication skills (oral and written) to explain complex algorithms, solutions to stakeholders across multiple disciplines, and ability to work in a diverse team

We’re looking to hire someone to help scale Machine Learning and NLP efforts at Episource. You’ll work with the team that develops the models powering Episource’s product focused on NLP driven medical coding. Some of the problems include improving our ICD code recommendations , clinical named entity recognition and information extraction from clinical notes.

This is a role for highly technical machine learning & data engineers who combine outstanding oral and written communication skills, and the ability to code up prototypes and productionalize using a large range of tools, algorithms, and languages. Most importantly they need to have the ability to autonomously plan and organize their work assignments based on high-level team goals.

You will be responsible for setting an agenda to develop and ship machine learning models that positively impact the business, working with partners across the company including operations and engineering. You will use research results to shape strategy for the company, and help build a foundation of tools and practices used by quantitative staff across the company.

What you will achieve:

• Define the research vision for data science, and oversee planning, staffing, and prioritization to make sure the team is advancing that roadmap

• Invest in your team’s skills, tools, and processes to improve their velocity, including working with engineering counterparts to shape the roadmap for machine learning needs

• Hire, retain, and develop talented and diverse staff through ownership of our data science hiring processes, brand, and functional leadership of data scientists

• Evangelise machine learning and AI internally and externally, including attending conferences and being a thought leader in the space

• Partner with the executive team and other business leaders to deliver cross-functional research work and models

Required Skills:

• Strong background in classical machine learning and machine learning deployments is a must and preferably with 4-8 years of experience

• Knowledge of deep learning & NLP

• Hands-on experience in TensorFlow/PyTorch, Scikit-Learn, Python, Apache Spark & Big Data platforms to manipulate large-scale structured and unstructured datasets.

• Experience with GPU computing is a plus.

• Professional experience as a data science leader, setting the vision for how to most effectively use data in your organization. This could be through technical leadership with ownership over a research agenda, or developing a team as a personnel manager in a new area at a larger company.

• Expert-level experience with a wide range of quantitative methods that can be applied to business problems.

• Evidence you’ve successfully been able to scope, deliver and sell your own research in a way that shifts the agenda of a large organization.

• Excellent written and verbal communication skills on quantitative topics for a variety of audiences: product managers, designers, engineers, and business leaders.

• Fluent in data fundamentals: SQL, data manipulation using a procedural language, statistics, experimentation, and modeling

Qualifications

• Professional experience as a data science leader, setting the vision for how to most effectively use data in your organization

• Expert-level experience with machine learning that can be applied to business problems

• Evidence you’ve successfully been able to scope, deliver and sell your own work in a way that shifts the agenda of a large organization

• Fluent in data fundamentals: SQL, data manipulation using a procedural language, statistics, experimentation, and modeling

• Degree in a field that has very applicable use of data science / statistics techniques (e.g. statistics, applied math, computer science, OR a science field with direct statistics application)

• 5+ years of industry experience in data science and machine learning, preferably at a software product company

• 3+ years of experience managing data science teams, incl. managing/grooming managers beneath you

• 3+ years of experience partnering with executive staff on data topics