Machine Learning (ML)
Data Science
Deep Learning- Work closely with your business to identify issues and use data to propose solutions for effective decision making
- Build algorithms and design experiments to merge, manage, interrogate and extract data to supply tailored reports to colleagues, customers or the wider organisation.
- Creating and using advanced machine learning algorithms and statistics: regression, simulation, scenario analysis, modeling, clustering, decision trees, neural networks, etc
- Querying databases and using statistical computer languages: R, Python, SLQ, etc.
- Visualizing/presenting data through various Dashboards for Data Analysis, Using Python Dash, Flask etc.
- Test data mining models to select the most appropriate ones for use on a project
- Work in a POSIX/UNIX environment to run/deploy applications
- Mine and analyze data from company databases to drive optimization and improvement of product development, marketing techniques and business strategies.
- Develop custom data models and algorithms to apply to data sets.
- Use predictive modeling to increase and optimize customer experiences, revenue generation, ad targeting and other business outcomes.
- Assess the effectiveness of data sources and data-gathering techniques and improve data collection methods
- Horizon scan to stay up to date with the latest technology, techniques and methods
- Coordinate with different functional teams to implement models and monitor outcomes.
- Stay curious and enthusiastic about using algorithms to solve problems and enthuse others to see the benefit of your work.
General Expectations:
- Able to create algorithms to extract information from large data sets
- Strong knowledge of Python, R, Java or another scripting/statistical languages to automate data retrieval, manipulation and analysis.
- Experience with extracting and aggregating data from large data sets using SQL or other tools
- Strong understanding of various NLP, and NLU techniques like Named Entity Recognition, Summarization, Topic Modeling, Text Classification, Lemmatization and Stemming.
- Knowledge and experience in statistical and data mining techniques: GLM/Regression, Random Forest, Boosting, Trees, etc.
- Experience with Python libraries such as Pandas, NumPy, SciPy, Scikit-Learn
- Experience with Jupyter / Pandas / Numpy to manipulate and analyse data
- Knowledge of Machine Learning techniques and their respective pros and cons
- Strong Knowledge of various Data Science Visualization Tools like Tableau, PowerBI, D3, Plotly, etc.
- Experience using web services: Redshift, AWS, S3, Spark, DigitalOcean, etc.
- Proficiency in using query languages, such as SQL, Spark DataFrame API, etc.
- Hands-on experience in HTML, CSS, Bootstrap, JavaScript, AJAX, jQuery and Prototyping.
- Hands-on experience on C#, Javascript, .Net
- Experience in understanding and analyzing data using statistical software (e.g., Python, R, KDB+ and other relevant libraries)
- Experienced in building applications that meet enterprise needs – secure, scalable, loosely coupled design
- Strong knowledge of computer science, algorithms, and design patterns
- Strong oral and written communication, and other soft skills critical to collaborating and engage with teams
Shubham Vishwakarma
Full Stack Developer - Averlon
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Amazon Web Services (AWS)
Follow CutshortShubham Vishwakarma
Full Stack Developer - Averlon