HCLTech Mega Walk in Drive

Date : Aug 22

Location - Bangalore
Hyderabad
Chennai

Experience - 5 years to 13 years

Please mark your attendance for the Walkin Drive below

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Job Overview:
We are seeking a skilled Machine Learning Engineer , Data Scientist , or Data Analyst to design, develop, and deploy machine learning models, conduct deep data analysis, and generate actionable insights. The ideal candidate will have experience in data preprocessing, feature engineering, model development, and performance optimization, working with large datasets and leveraging advanced machine learning frameworks.

Key Responsibilities
:Data Preparation & Analysi s
:Gather, clean, and preprocess structured, semi-structured, and unstructured data from various sources
.Conduct exploratory data analysis (EDA) to identify trends, patterns, and outliers
.Apply data wrangling techniques using Panda s, NumP y, and SQ L to transform raw data into usable formats
.Use statistical analysis to drive data-driven decision-making
.Machine Learning Model Developmen t
:Build, train, and fine-tune machine learning models using Scikit-lear n, TensorFlo w, Kera s, or PyTorc h
.Develop predictive models, classification algorithms, clustering models, and recommendation systems
.Conduct hyperparameter optimization using techniques like grid search or random search
.Model Evaluation & Optimizatio n
:Evaluate model performance using metrics such as Accurac y, Precisio n, Recal l, F1-Scor e, AUC-RO C, Confusion Matri x, and Cross-validatio n
.Improve model performance through techniques such as feature engineering, data augmentation, and regularization
.Deploy models into production environments,



and monitor performance for continual improvement
.Data Visualization & Reportin g
:Develop dashboards and reports using Tablea u, Power B I, Matplotli b, Seabor n, or Plotl y
.Present findings through clear visualizations and actionable insights to non-technical stakeholders
.Write detailed reports on data analysis and machine learning results, ensuring transparency and reproducibility
.Collaboration & Stakeholder Communicatio n
:Work closely with cross-functional teams (e.g., engineering, product, business) to define data-driven solutions
.Communicate technical concepts clearly to non-technical stakeholders and provide insights that influence product and business strategy
.Data Pipeline & Automatio n
:Design and implement scalable data pipelines for model training and deployment using Airflo w, Apache Kafk a, or Celer y
.Automate data collection, preprocessing, and feature extraction tasks
.Research & Continuous Learnin g
:Stay up-to-date with the latest trends in machine learning, deep learning, and data science methodologies
.Explore new tools, techniques, and frameworks to improve model accuracy and efficiency

.
Required Skill
s:Programming Languag es: Strong proficiency i n Pyth on, with experience i n S Q
L.Machine Learni ng: Hands-on experience wit h Scikit-lea rn , TensorFl ow , Ker as , PyTor ch, or similar ML librarie
s.Data Analys is: Robust skills i n Pand as , Num Py,



an d Matplotl ib for data manipulation and analysi
s.Statistical Analys is: Experience applying statistical methods to data, including hypothesis testing and regression analysi
s.Cloud Platfor ms: Familiarity wit h A WS , Azu re, o r Google Clo ud for deploying models and using cloud-native data services (e.g. , AWS Sagemak er , Azure ML
).Data Visualizati on: Experience usin g Table au , Power BI , Matplotl ib , Seabo rn, o r Plot ly for creating visualization
s.SQL & Databas es: Proficiency i n S QL for querying relational databases and working wit h NoS QL databases (e.g. , Mongo DB , BigQue ry
).Version Contr ol: Experience usin g G it for version contro

l.
Desirable Skil
ls:Big Data Technolog ies: Familiarity with tools li ke Apache Had oo p, Sp ar k, D ask, or Google BigQu ery for processing large datase
ts.Deep Learn ing: Experience with deep learning frameworks such as TensorF lo w, PyTo rch, or MX N
et.NLP & Computer Vis ion: Experience with natural language processing (NLP) usi ng sp aC y, N LTK, or transform ers, and computer vision usi ng Ope nCV or TensorF l
ow.ML Ops: Familiarity with MLOps tools li ke Kubef lo w, MLf low, or DVC for managing model workflo
ws.Data Engineer ing: Experience with ETL tools li ke Apache Airf lo w, Tal en d, AWS G lue, or Google Dataf low for data pipeline automati

on.
Tools & Technolog
ies:Machine Lear ni ng: Scikit-l ea rn, Tensor Fl ow, PyT or ch, K er as, XGB o
ost.Data Anal ys is: Pa nd as, N um Py, Matplo tl ib, Sea bo rn, Pl o
tly.Cloud Platf or ms: A WS, Google C lo ud, A z
ure.Datab as es: M yS QL, Postgr eS QL, Mon go DB, BigQ ue ry, Snowf l
ake.Data Visualiza ti on: Tab le au, Powe r BI, Matplo tl ib, Sea bo rn, Pl o
tly.Version Con tr ol:

Git.

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