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PFB the Job Description for Data Science with ML
Title : Data Scientist - Machine Learning
Experience : 5+ Yrs
Location : Chennai /Bengaluru
Type of hire : PWD and Non PWD
Employment Type : Full Time
Notice Period : Immediate Joiner
Working hours : 09:00 a.m. to 06:00 p.m.
Work Days : Mon - Fri
About Ampera:
Ampera Technologies, a purpose driven Digital IT Services with primary focus on supporting our client with their Data, AI / ML, Accessibility and other Digital IT needs. We also ensure that equal opportunities are provided to Persons with Disabilities Talent. Ampera Technologies has its Global Headquarters in Chicago, USA and its Global Delivery Center is based out of Chennai, India. We are actively expanding our Tech Delivery team in Chennai and across India. We offer exciting benefits for our teams, such as 1) Hybrid and Remote work options available, 2) Opportunity to work directly with our Global Enterprise Clients, 3) Opportunity to learn and implement evolving Technologies, 4) Comprehensive healthcare, and 5) Conducive environment for Persons with Disability Talent meeting Physical and Digital Accessibility standards
About the Role
We are looking for a skilled Data Scientist with strong Machine Learning experience to design, develop, and deploy data-driven solutions. The role involves working with large datasets, building predictive and ML models, and collaborating with cross-functional teams to translate business problems into analytical solutions.
Key Responsibilities
- Analyze large, structured and unstructured datasets to derive actionable insights.
- Design, build, validate, and deploy Machine Learning models for prediction, classification, recommendation, and optimization.
- Apply statistical analysis, feature engineering, and model evaluation techniques.
- Work closely with business stakeholders to understand requirements and convert them into data science solutions.
- Develop end-to-end ML pipelines including data preprocessing, model training, testing, and deployment.
- Monitor model performance and retrain models as required.
- Document assumptions, methodologies, and results clearly.
- Collaborate with data engineers and software teams to integrate models into production systems.
- Stay updated with the latest advancements in data science and machine learning.
Required Skills & Qualifications
- Bachelor’s or Master’s degree in computer science, Data Science, Statistics, Mathematics, or related fields.
- 5+ years of hands-on experience in Data Science and Machine Learning.
- Strong proficiency in Python (NumPy, Pandas, Scikit-learn).
- Experience with ML algorithms:
- Regression, Classification, Clustering
- Decision Trees, Random Forest, Gradient Boosting
- SVM, KNN, Naïve Bayes
- Solid understanding of statistics, probability, and linear algebra.
- Experience with data visualization tools (Matplotlib, Seaborn, Power BI, Tableau – preferred).
- Experience working with SQL and relational databases.
- Knowledge of model evaluation metrics and optimization techniques.
Preferred / Good to Have
- Experience with Deep Learning frameworks (TensorFlow, PyTorch, Keras).
- Exposure to NLP, Computer Vision, or Time Series forecasting.
- Experience with big data technologies (Spark, Hadoop).
- Familiarity with cloud platforms (AWS, Azure, GCP).
- Experience with MLOps, CI/CD pipelines, and model deployment.
Soft Skills
- Strong analytical and problem-solving abilities.
- Excellent communication and stakeholder interaction skills.
- Ability to work independently and in cross-functional teams.
- Curiosity and willingness to learn new tools and techniques.
Accessibility & Inclusion Statement
We are committed to creating an inclusive environment for all employees, including persons with disabilities. Reasonable accommodations will be provided upon request.
Equal Opportunity Employer (EOE) Statement
Ampera Technologies is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.
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