Z-3074 | Data Analyst (AI & ML Techniques for Large Data Sets)
Industry : Business
Sub Department : Others - General
Department : Others - General
: Goregaon East
: Mumbai
: India
: 27 - 30 p.a (INR Lacs)
: 2 - 5 Year
Job Preference :
Working Shift/ arrangement: US Shift (1:30 PM – 10:30 PM – IST)
Job Description :
We are looking for a skilled Data Analyst with expertise in working with large data sets, leveraging AI (Artificial Intelligence) and ML (Machine Learning) techniques to extract insights, build predictive models, and deliver actionable business solutions. The ideal candidate will possess strong analytical skills, technical proficiency, and a passion for working with data to solve complex problems.
Job Description (Primary Responsibilities)
1. Data Collection and Preparation:
Collect, clean, and preprocess large volumes of structured and unstructured data from multiple sources.
Develop and maintain data pipelines to ensure the accurate and efficient processing of data.
Handle data inconsistencies, missing values, and outliers, ensuring high-quality, usable datasets for analysis.
2. Exploratory Data Analysis (EDA):
Perform detailed exploratory data analysis (EDA) to understand data patterns, trends, and relationships.
Visualize data using tools such as Tableau or Power BI to identify key insights and drive decision-making.
3. AI and Machine Learning Model Development:
Apply AI and machine learning algorithms (e.g., classification, regression, clustering, deep learning) to build predictive and prescriptive models.
Train, tune, and evaluate machine learning models to optimize their performance.
Automate model deployment and update processes for continuous improvement in accuracy and efficiency.
4. Advanced Analytics:
Use statistical methods and machine learning techniques to forecast trends, identify anomalies, and detect patterns in large data sets.
Apply natural language processing (NLP) and computer vision techniques for text and image data analysis when needed.
5. Data Visualization and Reporting:
Communicate findings clearly through visualizations and reports to stakeholders across the organization.
Present complex data insights in an accessible and actionable format for both technical and non-technical audiences.
6. Collaboration and Communication:
Work closely with cross-functional teams to understand business needs and objectives.
Provide analytical support to business stakeholders by offering data-driven insights and recommendations.
7. Continuous Improvement:
Stay updated on the latest trends in AI/ML technologies and data analytics.
Continuously evaluate and implement new techniques to improve the quality and efficiency of analysis.
Explore new ways to automate repetitive tasks and improve business operations through data-driven solutions.
Qualification :
41, 53
Skills :
Desired Skills/Qualifications:
Education:
Engineer from tier 1 college(IITs/RECs/NSUT/DTU), Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field. MBA or CFA a plus
Experience: 2-5 years of experience in data analysis, data science, or machine learning roles, with hands-on experience in managing and analyzing large datasets
Skills:
Proficiency in Python, R, or similar programming languages for data analysis and machine learning.
Experience with machine learning frameworks such as TensorFlow, PyTorch, or scikitlearn.
Familiarity with AI techniques, including deep learning, reinforcement learning, and NLP.
Strong knowledge of SQL for querying large data sets.
Expertise in data visualization tools (e.g., Tableau, Power BI).
Experience with big data platforms and tools (e.g., Hadoop, Spark) is a plus.
Familiarity with cloud platforms (AWS, GCP, Azure) for data storage and processing.
Soft Skills:
Strong problem-solving and critical-thinking abilities.
Excellent communication and presentation skills.
Ability to work independently and as part of a team.
Attention to detail and the ability to work with complex data sets.
Preferred Qualifications:
Experience with deploying machine learning models to production environments.
Knowledge of advanced analytics techniques such as time series forecasting, anomaly detection, or reinforcement learning.
