Machine Learning Analyst

Data Analysis & Understanding:

· Extract, clean, and preprocess large datasets to prepare them for analysis.

· Analyze data patterns, trends, and anomalies to derive actionable insights.

· Analyze and interpret complex datasets to extract meaningful insights.

· Work closely with stakeholders to understand business requirements and translate them into data science solutions.

· Collaborate with data scientists, engineers, and stakeholders to understand requirements and deliver solutions.

Model Development & Deployment:

· Develop and implement machine learning models to solve business problems.

· Build, train, and evaluate machine learning models to address specific use cases such as classification, regression, or clustering.

· Perform feature engineering, model evaluation, and selection to improve model accuracy and performance.

· Deploy machine learning models into production and monitor their effectiveness over time.

· Monitor and maintain the performance of deployed models.

Collaboration & Communication:

· Partner with engineers to ensure seamless integration of machine learning solutions into existing systems.

· Communicate findings, insights, and results to non-technical stakeholders through reports and visualizations.

· Act as a key collaborator in brainstorming sessions to identify innovative approaches to challenges.

Performance Optimization:

· Conduct hyperparameter tuning and optimization of models to achieve desired performance metrics.

· Perform A/B testing and iterative experiments to continuously improve model outcomes.

Technology & Innovation:

· Stay informed about the latest trends, tools, and research in the field of machine learning and artificial intelligence.

· Recommend and implement new methodologies and technologies to improve workflow efficiency.

Consulting Responsibilities:

· Collaborate with clients to understand their unique challenges and define project objectives.

· Communicate technical concepts, findings, and insights to non-technical audiences, offering actionable recommendations.

· Develop and deliver impactful presentations, proposals, and reports to help clients make informed business decisions.

· Facilitate workshops and brainstorming sessions to identify new opportunities for machine learning applications.

· Build strong relationships with stakeholders and act as a liaison between technical teams and business units.

Required Qualifications & Skills:

Educational Background:

· Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science, Statistics, or a related field.

Technical Expertise:

· Strong understanding of machine learning algorithms (e.g., Random Forests, Gradient Boosting, Neural Networks) and statistical methods.

· Proficiency in Python, R, or similar programming languages for data analysis and model building.

· Hands-on experience with ML frameworks and libraries, such as TensorFlow, PyTorch, Scikit-learn, or Keras.

· Familiarity with big data tools such as Apache Spark, Hadoop, or similar platforms.

· Knowledge of database querying using SQL and experience with relational and non-relational databases.

Analytical & Problem-Solving Skills:

· Strong capability in exploratory data analysis and deriving insights.

· Experience in solving business problems using statistical techniques and machine learning.

Soft Skills:

· Exceptional communication skills to present technical concepts to non-technical audiences.

· Proven ability to work effectively in a collaborative, team-oriented environment.

· Self-motivated and detail-oriented, with the ability to manage multiple tasks and deadlines.

Consulting Skills:

· Proven experience in business consulting, data storytelling, and client engagement.

· Exceptional communication and interpersonal skills to manage client relationships effectively.

· Strong problem-solving and strategic thinking abilities to align technical solutions with business needs.

Preferred Qualifications:

· Experience working with cloud computing platforms such as AWS (SageMaker), Google Cloud (Vertex AI), or Microsoft Azure.

· Familiarity with Natural Language Processing (NLP) or Computer Vision techniques.

· Exposure to MLOps practices for streamlining workflows and model lifecycle management.

· Knowledge of data visualization tools like Tableau, Power BI, or matplotlib.

· Ability to work with cross-functional teams, including product managers and executives.

Key Performance Indicators (KPIs):

· Model accuracy, precision, recall, or other relevant metrics based on project requirements.

· Time taken to complete projects or deploy models into production.

· Value added to the business through actionable insights or automation achieved.

· Client satisfaction scores (e.g., through surveys or Net Promoter Score).

· Number of successful project completions within budget and timelines.

· Revenue growth or cost savings achieved for clients.

Job Type: Contractual / Temporary

Pay: ₹1,800, ₹2,000,000.00 per year

Application Question(s):

• What is your current Location ?
• What is your current CTC ?
• What is your Expected CTC ?
• What is your Notice Period ?

Experience:

• Machine learning: 2 years (Required)

Work Location: In person