Posted:13 hours ago|
Platform:
On-site
Full Time
• Educational Background: Bachelor’s degree in computer science / information technology, or master’s degree in Statistics, Economics, Mathematics.
• Demonstrated experience in independently driving business insights through data and AI.
• Technical Proficiency:
a. Hands-on experience with data tools (e.g., Oracle, SQL Server, MySQL, PostgreSQL, S3 buckets) with ability to work with large data sets.
b. Expertise in writing complex SQL queries for data mining, data manipulation and analysis.
c. Solid understanding of linear algebra, calculus, probability, and optimization techniques
d. Familiarity with cloud database services (AWS RDS, Azure SQL Database) is a must.
e. Solid understanding of Data Science – (K means, Clustering, Multivariate Regression, Logistic Regression, ML algorithms such as RF, GBM / XgBoost, SVM, NLP algos such as TFIDF, Word2vec, LDA, Exploratory Analytics (T-tests), Decision Trees. Familiarity with frameworks like TensorFlow, PyTorch, and Scikit-Learn is a plus.
• Knowledge of Data architecture principles and experience with AWS and GCP is a plus.
• Knowledge of Data Analysis and Visualization techniques is a plus. Soft Skills
• Experience in project management, stakeholder management, and leading multi-disciplinary teams for atleast 3-4 years.
• Problem-Solving Skills: Strong analytical and critical-thinking skills.
• Adaptability: Ability to adapt to innovative technologies and changing requirements • Excellent written and oral communication skills with strong ability of Visual storytelling
We're looking for a skilled senior Data Professional to contribute to our data analysis work. You will lead a team to work on analysing large internal and external data sets to derive meaningful insights for the organization.
1. Lead and manage multi-disciplinary teams (domain knowledge experts, data scientists, AI and technology specialists) for development of relevant solutions and insights.
2. Providing domain expertise for building future ready ideas and solutions
3. Leverage data cleaning and organizing techniques to develop datasets for analysis.
4. Define business hypothesis and analyse data to validate such hypothesis.
5. Mine data to identify patterns and create insights to help drive key business decisions.
6. Create compelling stories/ narratives using data to help expand the scale and impact of HQ work.
7. Scripting and Automation (e.g., PowerShell, Python) for automating data analysis tasks.
8. Working with internal and external partners.
Haqdarshak
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