Data Science
Python
Deep Learning
Machine Learning (ML)
Subodh Popalwar
Software Engineer, Memorres
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Data AnalyticsPythonA Delhi NCR based Applied AI & Consumer Tech company tackling one of the largest unsolved consumer internet problems of our time. We are a motley crew of smart, passionate and nice people who believe you can build a high performing company with a culture of respect aka a sports team with a heart aka a caring meritocracy.
Our illustrious angels include unicorn founders, serial entrepreneurs with exits, tech & consumer industry stalwarts and investment professionals/bankers.
We are hiring for our founding team (in Delhi NCR only, no remote) that will take the product from prototype to a landing! Opportunity for disproportionate non-linear impact, learning and wealth creation in a classic 0-1 with a Silicon Valley caliber founding team.
Key Responsibilities:
1. Data Strategy and Vision:
· Develop and drive the company's data analytics strategy, aligning it with overall business goals.
· Define the vision for data analytics, outlining clear objectives and key results (OKRs) to measure success.
2. Data Analysis and Interpretation:
· Oversee the analysis of complex datasets to extract valuable insights, trends, and patterns.
· Utilize statistical methods and data visualization techniques to present findings in a clear and compelling manner to both technical and non-technical stakeholders.
3. Data Infrastructure and Tools:
· Evaluate, select, and implement advanced analytics tools and platforms to enhance data processing and analysis capabilities.
· Collaborate with IT teams to ensure a robust and scalable data infrastructure, including data storage, retrieval, and security protocols.
4. Collaboration and Stakeholder Management:
· Collaborate cross-functionally with teams such as marketing, sales, and product development to identify opportunities for data-driven optimizations.
· Act as a liaison between technical and non-technical teams, ensuring effective communication of data insights and recommendations.
5. Performance Measurement:
· Establish key performance indicators (KPIs) and metrics to measure the impact of data analytics initiatives on business outcomes.
· Continuously assess and improve the accuracy and relevance of analytical models and methodologies.
Qualifications:
- Bachelor's or Master's degree in Data Science, Statistics, Computer Science, or related field.
- Proven experience (5+ years) in data analytics, with a focus on leading analytics teams and driving strategic initiatives.
- Proficiency in data analysis tools such as Python, R, SQL, and advanced knowledge of data visualization tools.
- Strong understanding of statistical methods, machine learning algorithms, and predictive modelling techniques.
- Excellent communication skills, both written and verbal, to effectively convey complex findings to diverse audie
Machine Learning (ML)Deep LearningData ScienceResponsibilities
- Work on execution and scheduling of all tasks related to assigned projects' deliverable dates
- Optimize and debug existing codes to make them scalable and improve performance
- Design, development, and delivery of tested code and machine learning models into production environments
- Work effectively in teams, managing and leading teams
- Provide effective, constructive feedback to the delivery leader
- Manage client expectations and work with an agile mindset with machine learning and AI technology
- Design and prototype data-driven solutions
Eligibility
- Highly experienced in designing, building, and shipping scalable and production-quality machine learning algorithms in the field of Python applications
- Working knowledge and experience in NLP core components (NER, Entity Disambiguation, etc.)
- In-depth expertise in Data Munging and Storage (Experienced in SQL, NoSQL, MongoDB, Graph Databases)
- Expertise in writing scalable APIs for machine learning models
- Experience with maintaining code logs, task schedulers, and security
- Working knowledge of machine learning techniques, feed-forward, recurrent and convolutional neural networks, entropy models, supervised and unsupervised learning
- Experience with at least one of the following: Keras, Tensorflow, Caffe, or PyTorch
Machine Learning (ML)Data Science- Lead the data science, ML, product analytics, and insights functions by translating sparse and decentralized datasets to develop metrics, standardize processes, and lead the path from data to insights.
- Building visualizations, models, pipelines, alerts/insights systems, and recommendations in Python/Java to support business decisions and operational experiences.
- Advising executives on calibration strategy, DEI, and workforce planning.
CORE RESPONSIBILITIES
- Create and manage cloud resources in AWS
- Data ingestion from different data sources which exposes data using different technologies, such as: RDBMS, REST HTTP API, flat files, Streams, and Time series data based on various proprietary systems. Implement data ingestion and processing with the help of Big Data technologies
- Data processing/transformation using various technologies such as Spark and Cloud Services. You will need to understand your part of business logic and implement it using the language supported by the base data platform
- Develop automated data quality check to make sure right data enters the platform and verifying the results of the calculations
- Develop an infrastructure to collect, transform, combine and publish/distribute customer data.
- Define process improvement opportunities to optimize data collection, insights and displays.
- Ensure data and results are accessible, scalable, efficient, accurate, complete and flexible
- Identify and interpret trends and patterns from complex data sets
- Construct a framework utilizing data visualization tools and techniques to present consolidated analytical and actionable results to relevant stakeholders.
- Key participant in regular Scrum ceremonies with the agile teams
- Proficient at developing queries, writing reports and presenting findings
- Mentor junior members and bring best industry practices
QUALIFICATIONS
- 5-7+ years’ experience as data engineer in consumer finance or equivalent industry (consumer loans, collections, servicing, optional product, and insurance sales)
- Strong background in math, statistics, computer science, data science or related discipline
- Advanced knowledge one of language: Java, Scala, Python, C#
- Production experience with: HDFS, YARN, Hive, Spark, Kafka, Oozie / Airflow, Amazon Web Services (AWS), Docker / Kubernetes, Snowflake
- Proficient with
- Data mining/programming tools (e.g. SAS, SQL, R, Python)
- Database technologies (e.g. PostgreSQL, Redshift, Snowflake. and Greenplum)
- Data visualization (e.g. Tableau, Looker, MicroStrategy)
- Comfortable learning about and deploying new technologies and tools.
- Organizational skills and the ability to handle multiple projects and priorities simultaneously and meet established deadlines.
- Good written and oral communication skills and ability to present results to non-technical audiences
- Knowledge of business intelligence and analytical tools, technologies and techniques.
Mandatory Requirements
- Experience in AWS Glue
- Experience in Apache Parquet
- Proficient in AWS S3 and data lake
- Knowledge of Snowflake
- Understanding of file-based ingestion best practices.
- Scripting language - Python & pyspark
Data SciencePythonMachine Learning (ML)- Working closely with business stakeholders to define, strategize and execute crucial business problem statements which lie at the core of improvising current and future data-backed product offerings.
- Building and refining underwriting models for extending credit to sellers and API Partners in collaboration with the lending team
- Conceiving, planning and prioritizing data projects and manage timelines
- Building analytical systems and predictive models as a part of the agile ecosystem
- Testing performance of data-driven products participating in sprint-wise feature releases
- Managing a team of data scientists and data engineers to develop, train and test predictive models
- Managing collaboration with internal and external stakeholders
- Building data-centric culture from within, partnering with every team, learning deeply about business, working with highly experienced, sharp and insanely ambitious colleagues
What you need to have:
- B.Tech/ M.Tech/ MS/ PhD in Data Science / Computer Science, Statistics, Mathematics & Computation with a demonstrated skill-set in leading an Analytics and Data Science team from IIT, BITS Pilani, ISI
- 8+ years working in the Data Science and analytics domain with 3+ years of experience in leading a data science team to understand the projects to be prioritized, how the team strategy aligns with the organization mission;
- Deep understanding of credit risk landscape; should have built or maintained underwriting models for unsecured lending products
- Should have handled a leadership team in a tech startup preferably a fintech/ lending/ credit risk startup.
- We value entrepreneurship spirit: if you have had the experience of starting your own venture - that is an added advantage.
- Strategic thinker with agility and endurance
- Aware of the latest industry trends in Data Science and Analytics with respect to Fintech, Digital Transformations and Credit-lending domain
- Excellent command over communication is the key to manage multiple stakeholders like the leadership team, product teams, existing & new investors.
- Cloud Computing, Python, SQL, ML algorithms, Analytics and problem - solving mindset
- Knowledge and demonstrated skill-sets in AWS
• 2+ years of experience in data engineering & strong understanding of data engineering principles using big data technologies
• Excellent programming skills in Python is mandatory
• Expertise in relational databases (MSSQL/MySQL/Postgres) and expertise in SQL. Exposure to NoSQL such as Cassandra. MongoDB will be a plus.
• Exposure to deploying ETL pipelines such as AirFlow, Docker containers & Lambda functions
• Experience in AWS loud services such as AWS CLI, Glue, Kinesis etc
• Experience using Tableau for data visualization is a plus
• Ability to demonstrate a portfolio of projects (GitHub, papers, etc.) is a plus
• Motivated, can-do attitude and desire to make a change is a must
• Excellent communication skills
Java
Job Description for :
Role: Data/Integration Architect
Experience – 8-10 Years
Notice Period: Under 30 days
Key Responsibilities: Designing, Developing frameworks for batch and real time jobs on Talend. Leading migration of these jobs from Mulesoft to Talend, maintaining best practices for the team, conducting code reviews and demos.
Core Skillsets:
Talend Data Fabric - Application, API Integration, Data Integration. Knowledge on Talend Management Cloud, deployment and scheduling of jobs using TMC or Autosys.
Programming Languages - Python/Java
Databases: SQL Server, Other Databases, Hadoop
Should have worked on Agile
Sound communication skills
Should be open to learning new technologies based on business needs on the job
Additional Skills:
Awareness of other data/integration platforms like Mulesoft, Camel
Awareness Hadoop, Snowflake, S3
Machine Learning (ML)Deep LearningJob Description :
Sr. Machine Learning Engineer will support our various business vertical teams with insights gained from analyzing company data. The ideal candidate is adept at using large data sets to find opportunities for product and process optimization and using models to test the effectiveness of different courses of action. They must have strong experience using a variety of data mining/data analysis methods, using a variety of data tools, building and implementing models, using/creating algorithms and creating/running simulations. They must have a proven ability to drive business results with their data-based insights. They must be comfortable working with a wide range of stakeholders and functional teams. The right candidate will have a passion for discovering solutions hidden in large data sets and working with stakeholders to improve business outcomes.
Accountabilities :
- Collaborate with product management and engineering departments to understand company needs and devise possible solutions
- Keep up-to-date with latest technology trends
- Communicate results and ideas to key decision makers
- Implement new statistical or other mathematical methodologies as needed for specific models or analysis
- Optimize joint development efforts through appropriate database use and project design
Skills & Requirements :
Technical Skills :
- Demonstrated skill in the use of one or more analytic software tools or languages (e.g., R, Python, Pyomo, Julia/Jump, Matlab, SAS,SQL)
- Demonstrated skill at data cleansing, data quality assessment, and using analytics for data assessment
- End-to-end system design: data analysis, feature engineering, technique selection & implementation, debugging, and maintenance in production.
- Profound understanding of skills like outlier handling, data imputation, bias, variance, cross validation etc.
- Demonstrated skill in modeling techniques, including but not limited to Predictive modeling, Supervised learning, Unsupervised learning, Machine Learning, Statistical Modeling, Natural language processing, Recommendation engines,
- Demonstrated skill in analytic prototyping, analytic scaling, and solutions integration
- Developing hypotheses and set up your own problem frameworks to test for the best solutions
- Knowledge of data visualization tools - ggplot, Dash, d3.js and Matplottlib (or any other data visualization like Tableau, Qlikview)
- Generating insights for a business context
Desirable :
- Experience with cloud technologies for building, deploying and delivering data science applications is desired (preferably in Microsoft Azure)
- Experience in Tensorflow, Keras, Theano, Text Mining is desirable but not mandatory
- Experience to work in Agile and DevOps processes.
Core Skills :
- Bachelor or master degree in information technology, computer science, business administration or a related discipline.
- Certified in Agile Product Owner / SCRUM master and/or other Agile techniques
Leadership Skills :
- Strong stakeholder management and influencing skills. Able to articulate a vision and build support for that vision in the wider team and organization.
- Ability to self-start and direct efforts based on high-level business objectives
- Strong collaboration and leadership skills with the ability to coach and develop teams to meet new challenges.
- Strong interpersonal, communication, facilitation and presentation skills.
- Work through complex interfaces across organizational and geographic boundaries
- Excellent analytical, planning and problem solving skills
Job Experience Requirements :
- Utilize an advanced knowledge level of the Data Science Toolbox to participate in the entire Data Science Project Life cycle and execute end-to-end Data Science project
- Work end-to-end on Data Science developments contributing to all aspects of the project life cycle
- Keep customers as focus of analysis insight and recommendation.
- Help define business objectives/customer needs by capturing the right requirements from the right customers.
- Can take defined problems and identify resolution paths and opportunities to solve them; which you validate by defining hypotheses and driving experiments
- Can identify unstructured problems and articulate opportunities to form new analytics project ideas
- Use and understand the key performance indicators (KPIs) and diagnostics to measure performance against business goals
- Compile integrate and analyze data from multiple sources to identify trends expose new opportunities and answer ongoing business questions
- Execute hypothesis-driven analysis to address business questions issues and opportunities
- Build validate and manage advanced models (e.g. explanatory predictive) using statistical and/or other analytical methods
- Are familiar working within Agile Project Management methodologies / structures
- Analyze results using statistical methods and work with senior team members to make recommendations to improve customer experience and business results
- Have the ability to conceptualize formulate prototype and implement algorithms to capture customer behavior and solve business problems
- Analyze results using statistical methods to make recommendations to improve customer experience and business results
Data ScienceWe are a nascent quantitative hedge fund led by an MIT PhD and Math Olympiad medallist, offering opportunities to grow with us as we build out the team. Our fund has world class investors and big data experts as part of the GP, top-notch ML experts as advisers to the fund, plus has equity funding to grow the team, license data and scale the data processing.
We are interested in researching and taking in live a variety of quantitative strategies based on historic and live market data, alternative datasets, social media data (both audio and video) and stock fundamental data.
You would join, and, if qualified, lead a growing team of data scientists and researchers, and be responsible for a complete lifecycle of quantitative strategy implementation and trading.
Requirements:
- Atleast 3 years of relevant ML experience
- Graduation date : 2018 and earlier
- 3-5 years of experience in high level Python programming.
- Master Degree (or Phd) in quantitative disciplines such as Statistics, Mathematics, Physics, Computer Science in top universities.
- Good knowledge of applied and theoretical statistics, linear algebra and machine learning techniques.
- Ability to leverage financial and statistical insights to research, explore and harness a large collection of quantitative strategies and financial datasets in order to build strong predictive models.
- Should take ownership for the research, design, development and implementation of the strategy development and effectively communicate with other team mates
- Prior experience and good knowledge of lifecycle and pitfalls of algorithmic strategy development and modelling.
- Good practical knowledge in understanding financial statements, value investing, portfolio and risk management techniques.
- A proven ability to lead and drive innovation to solve challenges and road blocks in project completion.
- A valid Github profile with some activity in it
Bonus to have:
- Experience in storing and retrieving data from large and complex time series databases
- Very good practical knowledge on time-series modelling and forecasting (ARIMA, ARCH and Stochastic modelling)
- Prior experience in optimizing and back testing quantitative strategies, doing return and risk attribution, feature/factor evaluation.
- Knowledge of AWS/Cloud ecosystem is an added plus (EC2s, Lambda, EKS, Sagemaker etc.)
- Knowledge of REST APIs and data extracting and cleaning techniques
- Good to have experience in Pyspark or any other big data programming/parallel computing
- Familiarity with derivatives, knowledge in multiple asset classes along with Equities.
- Any progress towards CFA or FRM is a bonus
- Average tenure of atleast 1.5 years in a company
Java
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Software Engineer, Memorres