{"id":19136,"date":"2026-05-05T11:10:03","date_gmt":"2026-05-05T04:10:03","guid":{"rendered":"https:\/\/www.careerlink.vn\/cam-nang-viec-lam\/?p=19136"},"modified":"2026-07-16T13:48:36","modified_gmt":"2026-07-16T06:48:36","slug":"cau-hoi-phong-van-data-scientist","status":"publish","type":"post","link":"https:\/\/www.careerlink.vn\/cam-nang-viec-lam\/phong-van-viec-lam\/cau-hoi-phong-van-data-scientist","title":{"rendered":"30 c\u00e2u h\u1ecfi ph\u1ecfng v\u1ea5n Data Scientist \u2013 Stats, ML, Coding, System"},"content":{"rendered":"\n

Data Scientist l\u00e0 m\u1ed9t trong nh\u1eefng v\u1ecb tr\u00ed c\u00f3 nhu c\u1ea7u tuy\u1ec3n d\u1ee5ng v\u00e0 m\u1ee9c l\u01b0\u01a1ng cao nh\u1ea5t ng\u00e0nh CNTT t\u1ea1i Vi\u1ec7t Nam 2026. Theo d\u1eef li\u1ec7u CareerLink (05\/2026) v\u00e0 kh\u1ea3o s\u00e1t Talentnet 2025, l\u01b0\u01a1ng Data Scientist t\u1ea1i VN t\u0103ng 22% so v\u1edbi 2024 \u2013 cao th\u1ee9 2 trong c\u00e1c vai tr\u00f2 IT (sau ML Engineer). B\u1ed9 c\u00e2u h\u1ecfi ph\u1ecfng v\u1ea5n Data Scientist<\/strong> th\u01b0\u1eddng t\u1eadp trung v\u00e0o 5 nh\u00f3m: Statistics & Math, Machine Learning, Data Engineering, Programming (Python\/SQL), v\u00e0 Behavioral case. B\u00e0i vi\u1ebft t\u1ed5ng h\u1ee3p 30 c\u00e2u h\u1ecfi ph\u1ed5 bi\u1ebfn nh\u1ea5t v\u1edbi khung tr\u1ea3 l\u1eddi cho th\u1ecb tr\u01b0\u1eddng VN.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n
\n

T\u1ed5ng quan nhanh:<\/strong><\/p>\n

\u2013 Quy tr\u00ecnh ph\u1ecfng v\u1ea5n Data Scientist th\u01b0\u1eddng 4\u20135 v\u00f2ng: HR \u2192 Take-home assignment \u2192 Technical Interview \u2192 System Design \u2192 Hiring Manager.<\/p>\n

\u2013 5 nh\u00f3m c\u00e2u h\u1ecfi: Stats & ML (35%), Coding Python\/SQL (25%), Data Engineering (15%), System Design (15%), Behavioral (10%).<\/p>\n

\u2013 M\u1ee9c l\u01b0\u01a1ng 2026 (CRL Q2 + Talentnet): Junior 18\u201328 tri\u1ec7u, Mid 30\u201355 tri\u1ec7u, Senior 60\u2013100 tri\u1ec7u, Lead 90\u2013160 tri\u1ec7u.<\/p>\n

\u2013 Top 5 c\u00f4ng ty tuy\u1ec3n nhi\u1ec1u: VNG Cloud, MoMo, Be Group, FPT AI, VinAI Research.<\/p>\n<\/div>\n\n\n\n

1. Nh\u00f3m Statistics & Probability<\/h2>\n\n\n\n

\u0110\u00e2y l\u00e0 ki\u1ebfn th\u1ee9c n\u1ec1n \u2013 \u0111a s\u1ed1 ph\u1ecfng v\u1ea5n v\u00f2ng \u0111\u1ea7u h\u1ecfi 3\u20135 c\u00e2u nh\u00f3m n\u00e0y.<\/p>\n\n\n\n

\u2013 C\u00e2u 1:<\/strong> “S\u1ef1 kh\u00e1c bi\u1ec7t gi\u1eefa Type I v\u00e0 Type II error?”. Khung: Type I (false positive) \u2013 reject H0 khi \u0111\u00fang. Type II (false negative) \u2013 fail to reject H0 khi sai. Trade-off qua significance level \u03b1 v\u00e0 power 1-\u03b2.<\/p>\n\n\n\n

\u2013 C\u00e2u 2:<\/strong> “Khi n\u00e0o d\u00f9ng t-test, khi n\u00e0o d\u00f9ng z-test?”. Khung: z-test khi sample size > 30 v\u00e0 bi\u1ebft \u03c3. t-test khi sample size < 30 ho\u1eb7c kh\u00f4ng bi\u1ebft \u03c3. Both gi\u1ea3 \u0111\u1ecbnh data normally distributed.<\/p>\n\n\n\n

\u2013 C\u00e2u 3:<\/strong> “P-value l\u00e0 g\u00ec? Threshold th\u01b0\u1eddng l\u00e0?”. Khung: X\u00e1c su\u1ea5t quan s\u00e1t \u0111\u01b0\u1ee3c data extreme nh\u01b0 hi\u1ec7n t\u1ea1i n\u1ebfu H0 \u0111\u00fang. p < 0.05 th\u01b0\u1eddng d\u00f9ng \u0111\u1ec3 reject H0 (significance level \u03b1).<\/p>\n\n\n\n

\u2013 C\u00e2u 4:<\/strong> “Central Limit Theorem (CLT) ph\u00e1t bi\u1ec3u g\u00ec?”. Khung: Sampling distribution c\u1ee7a mean ti\u1ec7m c\u1eadn normal khi n \u0111\u1ee7 l\u1edbn (\u226530), b\u1ea5t k\u1ec3 distribution g\u1ed1c. C\u01a1 s\u1edf cho confidence interval v\u00e0 hypothesis testing.<\/p>\n\n\n\n

\u2013 C\u00e2u 5:<\/strong> “Kh\u00e1c bi\u1ec7t gi\u1eefa correlation v\u00e0 causation?”. Khung: Correlation: 2 bi\u1ebfn bi\u1ebfn thi\u00ean c\u00f9ng nhau (Pearson r). Causation: A g\u00e2y ra B. Correlation kh\u00f4ng implies causation \u2013 c\u1ea7n experiment (RCT) ho\u1eb7c quasi-experiment (DiD, IV).<\/p>\n\n\n\n

2. Nh\u00f3m Machine Learning<\/h2>\n\n\n\n
\n\n\n\n\n\n\n\n\n\n
C\u00e2u h\u1ecfi<\/th>\n\u0110i\u1ec3m tr\u1ecdng t\u00e2m<\/th>\n<\/tr>\n<\/thead>\n
Bias-variance tradeoff?<\/td>\nBias cao = underfit; variance cao = overfit. Total error = bias\u00b2 + variance + noise. C\u00e2n b\u1eb1ng qua regularization, cross-validation<\/td>\n<\/tr>\n
Khi n\u00e0o d\u00f9ng L1 vs L2 regularization?<\/td>\nL1 (Lasso) \u2013 feature selection, sparse model. L2 (Ridge) \u2013 t\u1ea5t c\u1ea3 feature, smooth weights. Elastic Net = L1 + L2<\/td>\n<\/tr>\n
ROC AUC vs Precision-Recall?<\/td>\nROC AUC t\u1ed1t cho balanced data. PR curve t\u1ed1t h\u01a1n cho imbalanced (fraud detection, churn)<\/td>\n<\/tr>\n
Random Forest vs XGBoost?<\/td>\nRF \u2013 bagging parallel, robust noise. XGBoost \u2013 boosting sequential, accuracy cao h\u01a1n nh\u01b0ng d\u1ec5 overfit, c\u1ea7n tuning<\/td>\n<\/tr>\n
Cross-validation strategies?<\/td>\nk-fold (k=5\/10), stratified k-fold cho classification, time-series split cho temporal data<\/td>\n<\/tr>\n
Class imbalance x\u1eed l\u00fd th\u1ebf n\u00e0o?<\/td>\nSMOTE oversampling, undersampling, class weight, threshold tuning, focal loss<\/td>\n<\/tr>\n<\/tbody>\n<\/table><\/figure>\n\n\n\n

3. Nh\u00f3m coding Python & SQL<\/h2>\n\n\n\n

\u0110\u00e2y l\u00e0 ph\u1ea7n th\u1ef1c h\u00e0nh \u2013 th\u01b0\u1eddng c\u00f3 live coding 1\u20132 b\u00e0i.<\/p>\n\n\n\n

\u2013 C\u00e2u 12 (Python):<\/strong> “Vi\u1ebft function t\u00ednh moving average c\u1ee7a list”. Khung: D\u00f9ng deque ho\u1eb7c rolling window. Code: def moving_avg(arr, w): return [sum(arr[i:i+w])\/w for i in range(len(arr)-w+1)]<\/code>. L\u01b0u \u00fd: pandas df.rolling(w).mean()<\/code> hi\u1ec7u qu\u1ea3 h\u01a1n cho large data.<\/p>\n\n\n\n

\u2013 C\u00e2u 13 (SQL):<\/strong> “Vi\u1ebft query t\u00ecm top 3 s\u1ea3n ph\u1ea9m doanh thu cao nh\u1ea5t m\u1ed7i th\u00e1ng”. Khung: D\u00f9ng ROW_NUMBER() OVER (PARTITION BY month ORDER BY revenue DESC). WHERE rn \u2264 3.<\/p>\n\n\n\n

\u2013 C\u00e2u 14 (Python):<\/strong> “Kh\u00e1c bi\u1ec7t list comprehension v\u00e0 generator?”. Khung: List \u2013 eval ngay, l\u01b0u memory to\u00e0n b\u1ed9. Generator \u2013 lazy eval, l\u01b0u state. Generator ph\u00f9 h\u1ee3p large data ho\u1eb7c streaming.<\/p>\n\n\n\n

\u2013 C\u00e2u 15 (SQL):<\/strong> “INNER JOIN vs LEFT JOIN kh\u00e1c g\u00ec?”. Khung: INNER \u2013 ch\u1ec9 rows match c\u1ea3 2 b\u1ea3ng. LEFT \u2013 t\u1ea5t c\u1ea3 rows t\u1eeb left + match t\u1eeb right (NULL n\u1ebfu kh\u00f4ng match). Quan tr\u1ecdng cho missing data analysis.<\/p>\n\n\n\n

\u2013 C\u00e2u 16 (Pandas):<\/strong> “Khi n\u00e0o d\u00f9ng pivot_table vs groupby?”. Khung: groupby cho aggregation \u0111\u01a1n gi\u1ea3n (sum\/mean\/count). pivot_table cho cross-tabulation v\u1edbi row + column index. pivot_table flexible h\u01a1n nh\u01b0ng ch\u1eadm h\u01a1n.<\/p>\n\n\n\n

Tham kh\u1ea3o c\u00e1c v\u1ecb tr\u00ed Data Scientist \u0111ang tuy\u1ec3n t\u1ea1i chuy\u00ean m\u1ee5c CNTT \u2013 ph\u1ea7n m\u1ec1m<\/a> tr\u00ean CareerLink \u0111\u1ec3 hi\u1ec3u r\u00f5 scope y\u00eau c\u1ea7u c\u1ee7a t\u1eebng c\u00f4ng ty top t\u1ea1i VN.<\/p>\n\n\n\n

4. Nh\u00f3m Data Engineering & Big Data<\/h2>\n\n\n\n

Ph\u1ea7n n\u00e0y quan tr\u1ecdng cho c\u1ea5p Mid+ v\u00e0 c\u00e1c c\u00f4ng ty x\u1eed l\u00fd big data (VNG, MoMo, Be).<\/p>\n\n\n\n

\u2013 C\u00e2u 17:<\/strong> “ETL vs ELT kh\u00e1c nhau?”. Khung: ETL \u2013 Extract, Transform, Load (transform tr\u01b0\u1edbc khi \u0111\u01b0a v\u00e0o DW). ELT \u2013 Load tr\u01b0\u1edbc, Transform sau (ph\u00f9 h\u1ee3p data lake, modern warehouse nh\u01b0 BigQuery, Snowflake).<\/p>\n\n\n\n

\u2013 C\u00e2u 18:<\/strong> “Khi n\u00e0o d\u00f9ng Spark thay v\u00ec Pandas?”. Khung: Pandas \u2013 data < 10GB, single machine. Spark \u2013 distributed, > 10GB ho\u1eb7c c\u1ea7n parallel processing. PySpark API t\u01b0\u01a1ng t\u1ef1 Pandas, d\u1ec5 chuy\u1ec3n.<\/p>\n\n\n\n

\u2013 C\u00e2u 19:<\/strong> “Schema evolution trong data lake x\u1eed l\u00fd th\u1ebf n\u00e0o?”. Khung: Schema-on-read (Parquet, Delta Lake), versioning (Iceberg), backward\/forward compatibility, schema registry (Confluent).<\/p>\n\n\n\n

\u2013 C\u00e2u 20:<\/strong> “Anh\/ch\u1ecb \u0111\u00e3 d\u00f9ng c\u00e1c tool n\u00e0o?”. Khung: Pipeline \u2013 Airflow, Prefect, Dagster. Storage \u2013 S3, BigQuery, Snowflake. Stream \u2013 Kafka, Flink, Kinesis. ML platform \u2013 MLflow, Kubeflow, Vertex AI.<\/p>\n\n\n\n

\n

“Ph\u1ecfng v\u1ea5n Data Scientist t\u1ea1i VN ng\u00e0y c\u00e0ng y\u00eau c\u1ea7u th\u1ef1c t\u1ebf. T\u00f4i kh\u00f4ng ch\u1ec9 h\u1ecfi ‘XGBoost l\u00e0 g\u00ec’ m\u00e0 y\u00eau c\u1ea7u \u1ee9ng vi\u00ean k\u1ec3 1 use case h\u1ecd \u0111\u00e3 build t\u1eeb \u0111\u1ea7u, t\u1eeb data collection, feature engineering, model selection \u0111\u1ebfn deployment v\u00e0 monitoring. \u0110\u00f3 m\u1edbi l\u00e0 Data Scientist th\u1eadt s\u1ef1.” \u2013 Head of Data Science m\u1ed9t fintech unicorn t\u1ea1i TP.HCM.<\/p>\n<\/blockquote>\n\n\n\n

5. Nh\u00f3m System Design \/ End-to-End ML Pipeline<\/h2>\n\n\n\n
\"\"<\/figure>\n\n\n\n

C\u00e2u h\u1ecfi c\u1ea5p Senior \u2013 \u0111\u00e1nh gi\u00e1 t\u01b0 duy h\u1ec7 th\u1ed1ng v\u00e0 kinh nghi\u1ec7m production.<\/p>\n\n\n\n

\u2013 C\u00e2u 21:<\/strong> “Design 1 h\u1ec7 th\u1ed1ng recommendation cho Shopee\/Lazada?”. Khung: Data sources (user behavior, product catalog, transaction). Pipeline: collect \u2192 feature store \u2192 train (collaborative filtering + content-based + deep learning) \u2192 serve (low latency < 100ms via cache + ANN search).<\/p>\n\n\n\n

\u2013 C\u00e2u 22:<\/strong> “Build fraud detection cho fintech \u2013 approach?”. Khung: Imbalanced data (1:1000+ ratio), feature engineering (velocity, amount distribution, network graph), model (XGBoost + Isolation Forest + Graph Neural Net), real-time scoring < 50ms, feedback loop.<\/p>\n\n\n\n

\u2013 C\u00e2u 23:<\/strong> “Anh\/ch\u1ecb monitor model performance trong production th\u1ebf n\u00e0o?”. Khung: Data drift (PSI, KS test), concept drift (performance metrics over time), prediction distribution, latency, throughput. Tools: Evidently, WhyLabs, custom dashboards.<\/p>\n\n\n\n

\u2013 C\u00e2u 24:<\/strong> “A\/B testing setup khi n\u00e0o kh\u00f4ng \u0111\u1ee7?”. Khung: Network effects (social platform), seasonal effects, kh\u00f4ng th\u1ec3 randomize (ride-sharing pricing). Alternative: switchback, geo-experiments, synthetic control.<\/p>\n\n\n\n

\u2013 C\u00e2u 25:<\/strong> “Khi n\u00e0o n\u00ean build model in-house vs d\u00f9ng API (OpenAI\/Anthropic)?”. Khung: In-house khi data sensitive, latency requirement < 100ms, custom domain. API khi prototype, scale nh\u1ecf, kh\u00f4ng c\u00f3 expertise NLP\/computer vision.<\/p>\n\n\n\n

6. Nh\u00f3m Behavioral case (STAR)<\/h2>\n\n\n\n

V\u00f2ng cu\u1ed1i v\u1edbi Hiring Manager ho\u1eb7c Director of Data Science.<\/p>\n\n\n\n

\u2013 C\u00e2u 26:<\/strong> “K\u1ec3 v\u1ec1 d\u1ef1 \u00e1n ML c\u00f3 business impact l\u1edbn nh\u1ea5t”. Khung STAR: V\u1ea5n \u0111\u1ec1 business, approach, k\u1ebft qu\u1ea3 \u0111\u1ecbnh l\u01b0\u1ee3ng (revenue lift, cost saving, NPS).<\/p>\n\n\n\n

\u2013 C\u00e2u 27:<\/strong> “L\u1ea7n model fail trong production \u2013 b\u00e0i h\u1ecdc?”. Khung: B\u1ed1i c\u1ea3nh, root cause, h\u00e0nh \u0111\u1ed9ng kh\u1eafc ph\u1ee5c, quy tr\u00ecnh m\u1edbi (monitoring, validation, rollback).<\/p>\n\n\n\n

\u2013 C\u00e2u 28:<\/strong> “Anh\/ch\u1ecb thuy\u1ebft ph\u1ee5c stakeholder kh\u00f4ng tin ML th\u1ebf n\u00e0o?”. Khung: B\u1eaft \u0111\u1ea7u v\u1edbi metric h\u1ecd care, simple model tr\u01b0\u1edbc (LR\/RF), MVP, prove ROI, scale d\u1ea7n.<\/p>\n\n\n\n

\u2013 C\u00e2u 29:<\/strong> “Anh\/ch\u1ecb l\u00e0m g\u00ec khi model \u0111\u1ea1t 95% accuracy nh\u01b0ng business kh\u00f4ng th\u1ea5y gi\u00e1 tr\u1ecb?”. Khung: Re-evaluate metric (accuracy \u2260 business value), align v\u1edbi revenue\/cost, \u0111o l\u01b0\u1eddng net benefit per prediction, A\/B test.<\/p>\n\n\n\n

\u2013 C\u00e2u 30:<\/strong> “Anh\/ch\u1ecb c\u00f3 c\u00e2u h\u1ecfi g\u00ec cho ch\u00fang t\u00f4i?”. Khung: Roadmap data team 12 th\u00e1ng, infra hi\u1ec7n t\u1ea1i (data lake, ML platform), v\u0103n ho\u00e1 experimentation, mentorship.<\/p>\n\n\n\n

\n

L\u1ed7i c\u1ea7n tr\u00e1nh khi ph\u1ecfng v\u1ea5n Data Scientist:<\/strong><\/p>\n

\u2013 Ch\u1ec9 k\u1ec3 v\u1ec1 Kaggle competition m\u00e0 thi\u1ebfu kinh nghi\u1ec7m production \u2013 c\u00f4ng ty mu\u1ed1n \u1ee9ng vi\u00ean hi\u1ec3u end-to-end.<\/p>\n

\u2013 Tr\u1ea3 l\u1eddi “t\u00f4i s\u1ebd d\u00f9ng XGBoost” cho m\u1ecdi v\u1ea5n \u0111\u1ec1 \u2013 c\u1ea7n explain reasoning l\u1ef1a ch\u1ecdn model.<\/p>\n

\u2013 B\u1ecf qua data quality v\u00e0 feature engineering \u2013 c\u00e1c b\u00e0i to\u00e1n th\u1ef1c 80% th\u1eddi gian l\u00e0 data prep.<\/p>\n

\u2013 Qu\u00e1 t\u1eadp trung deep learning khi v\u1ea5n \u0111\u1ec1 c\u00f3 th\u1ec3 gi\u1ea3i b\u1eb1ng model \u0111\u01a1n gi\u1ea3n.<\/p>\n

\u2013 Kh\u00f4ng c\u00f3 v\u00ed d\u1ee5 c\u1ee5 th\u1ec3 v\u1edbi business impact \u2013 r\u1ea5t quan tr\u1ecdng cho c\u1ea5p Senior.<\/p>\n<\/div>\n\n\n\n

7. Top c\u00f4ng ty tuy\u1ec3n Data Scientist 2026 t\u1ea1i VN<\/h2>\n\n\n\n
\n\n\n\n\n\n\n\n\n
C\u00f4ng ty<\/th>\nSenior 5\u20137 n\u0103m (tri\u1ec7u\/th\u00e1ng)<\/th>\n\u0110\u1eb7c th\u00f9<\/th>\n<\/tr>\n<\/thead>\n
VinAI Research<\/td>\n80\u2013140<\/td>\nResearch, AI Foundation Model<\/td>\n<\/tr>\n
MoMo Pay<\/td>\n65\u2013110<\/td>\nFintech, fraud detection, recommendation<\/td>\n<\/tr>\n
VNG Cloud<\/td>\n60\u2013100<\/td>\nGame analytics, cloud AI<\/td>\n<\/tr>\n
Be Group<\/td>\n55\u201395<\/td>\nRide-sharing pricing, demand forecasting<\/td>\n<\/tr>\n
FPT AI<\/td>\n50\u201390<\/td>\nNLP ti\u1ebfng Vi\u1ec7t, OCR, computer vision<\/td>\n<\/tr>\n<\/tbody>\n<\/table><\/figure>\n\n\n\n

Tham kh\u1ea3o th\u00eam b\u00e0i vi\u1ebft v\u1ec1 l\u1eadp tr\u00ecnh vi\u00ean<\/a> \u0111\u1ec3 hi\u1ec3u th\u00eam v\u1ec1 l\u1ed9 tr\u00ecnh ngh\u1ec1 nghi\u1ec7p ng\u00e0nh CNTT\/AI t\u1ea1i Vi\u1ec7t Nam.<\/p>\n\n\n\n

\n

L\u1eddi khuy\u00ean chu\u1ea9n b\u1ecb ph\u1ecfng v\u1ea5n Data Scientist:<\/strong><\/p>\n

\u2013 Practice tr\u00ean LeetCode (50+ medium SQL + Python), Stratascratch.<\/p>\n

\u2013 \u0110\u1ecdc 1 system design book: “Designing Machine Learning Systems” (Chip Huyen).<\/p>\n

\u2013 Build 2\u20133 portfolio project c\u00f3 business impact r\u00f5 r\u00e0ng (Kaggle GOLD\/SILVER, ho\u1eb7c d\u1ef1 \u00e1n freelance).<\/p>\n

\u2013 Chu\u1ea9n b\u1ecb 5 STAR stories: d\u1ef1 \u00e1n impact l\u1edbn, model fail, thuy\u1ebft ph\u1ee5c stakeholder, deal v\u1edbi data quality, mentor team.<\/p>\n

\u2013 H\u1ecdc AWS Certified Machine Learning ho\u1eb7c Google Professional ML Engineer \u2013 t\u0103ng gi\u00e1 tr\u1ecb CV.<\/p>\n<\/div>\n\n\n\n

8. C\u00e2u h\u1ecfi th\u01b0\u1eddng g\u1eb7p<\/h2>\n\n\n\n

1. Sinh vi\u00ean tr\u00e1i ng\u00e0nh (To\u00e1n, V\u1eadt l\u00fd, Kinh t\u1ebf) c\u00f3 th\u1ec3 v\u00e0o Data Scientist kh\u00f4ng?<\/em><\/strong><\/p>\n\n\n\n

C\u00f3. Ng\u00e0nh To\u00e1n, V\u1eadt l\u00fd, Kinh t\u1ebf L\u01b0\u1ee3ng c\u00f3 n\u1ec1n t\u1ea3ng Stats t\u1ed1t \u2013 chuy\u1ec3n sang DS d\u1ec5. C\u1ea7n \u0111\u1ea7u t\u01b0 6\u201312 th\u00e1ng h\u1ecdc Python (Pandas, NumPy, Scikit-learn), SQL, ML c\u01a1 b\u1ea3n. Path khuy\u1ebfn ngh\u1ecb: ho\u00e0n th\u00e0nh Coursera Data Science Specialization (Johns Hopkins) ho\u1eb7c Andrew Ng ML Course, build 2 portfolio Kaggle project, apply Junior Data Analyst tr\u01b0\u1edbc (12\u201324 th\u00e1ng) r\u1ed3i chuy\u1ec3n sang Data Scientist.<\/p>\n\n\n\n

2. C\u1ea7n bi\u1ebft Deep Learning \u0111\u1ec3 v\u00e0o Data Scientist kh\u00f4ng?<\/em><\/strong><\/p>\n\n\n\n

Kh\u00f4ng b\u1eaft bu\u1ed9c cho Junior. \u0110a s\u1ed1 use case business solve \u0111\u01b0\u1ee3c b\u1eb1ng XGBoost\/LightGBM (tabular data). Deep Learning quan tr\u1ecdng cho: NLP (BERT\/LLM), Computer Vision (CNN), Recommendation (Two-Tower). Junior n\u00ean master classical ML tr\u01b0\u1edbc (Linear\/Logistic Regression, Tree-based), h\u1ecdc DL khi \u0111\u00e3 solid foundation. Senior+ th\u00ec Deep Learning l\u00e0 k\u1ef9 n\u0103ng must-have.<\/p>\n\n\n\n

3. L\u01b0\u01a1ng Data Scientist Vi\u1ec7t Nam c\u00f3 cao h\u01a1n c\u00e1c vai tr\u00f2 IT kh\u00e1c kh\u00f4ng?<\/em><\/strong><\/p>\n\n\n\n

Cao h\u01a1n 15\u201325% so v\u1edbi Software Engineer c\u00f9ng c\u1ea5p. Senior Data Scientist 6 n\u0103m: 60\u2013100 tri\u1ec7u (vs Senior SE 50\u201390 tri\u1ec7u). L\u00fd do: nhu c\u1ea7u cao + supply \u00edt (c\u1ea7n combo Math\/Stats + Programming + Business sense). ML Engineer (DS + Engineering skills) cao nh\u1ea5t ng\u00e0nh \u2013 Senior 80\u2013150 tri\u1ec7u\/th\u00e1ng t\u1ea1i c\u00e1c c\u00f4ng ty top nh\u01b0 VinAI, MoMo, VNG.<\/p>\n\n\n\n

Chu\u1ea9n b\u1ecb t\u1ed1t cho c\u00e2u h\u1ecfi ph\u1ecfng v\u1ea5n Data Scientist<\/strong> \u0111\u00f2i h\u1ecfi \u0111\u1ea7u t\u01b0 nghi\u00eam t\u00fac cho 5 nh\u00f3m: Statistics & ML c\u01a1 b\u1ea3n, Coding Python\/SQL, Data Engineering, System Design end-to-end, v\u00e0 Behavioral case. M\u1ed9t \u1ee9ng vi\u00ean c\u00f3 2\u20133 portfolio project v\u1edbi business impact r\u00f5 r\u00e0ng + chu\u1ea9n b\u1ecb 5 STAR stories chu\u1ea9n s\u1ebd t\u1ea1o l\u1ee3i th\u1ebf quy\u1ebft \u0111\u1ecbnh trong v\u00f2ng ph\u1ecfng v\u1ea5n cu\u1ed1i t\u1ea1i c\u00e1c c\u00f4ng ty top nh\u01b0 VinAI, MoMo, VNG, Be Group.<\/p>\n\n\n\n

Minh An<\/strong><\/p>\n\n\n\n

B\u00e0i vi\u1ebft mang t\u00ednh ch\u1ea5t tham kh\u1ea3o, kh\u00f4ng thay th\u1ebf t\u01b0 v\u1ea5n ngh\u1ec1 nghi\u1ec7p chuy\u00ean s\u00e2u t\u1eeb chuy\u00ean gia ng\u00e0nh Data Science \/ AI.<\/em><\/p>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

T\u1ed5ng h\u1ee3p 30 c\u00e2u h\u1ecfi ph\u1ecfng v\u1ea5n Data Scientist t\u1ea1i VN 2026 v\u1edbi khung tr\u1ea3 l\u1eddi theo 5 nh\u00f3m: Stats, ML, Coding Python\/SQL, Data Engineering, Behavioral.<\/p>\n","protected":false},"author":58,"featured_media":19188,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[45],"tags":[],"class_list":["post-19136","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-phong-van-viec-lam"],"_links":{"self":[{"href":"https:\/\/www.careerlink.vn\/cam-nang-viec-lam\/wp-json\/wp\/v2\/posts\/19136","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.careerlink.vn\/cam-nang-viec-lam\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.careerlink.vn\/cam-nang-viec-lam\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.careerlink.vn\/cam-nang-viec-lam\/wp-json\/wp\/v2\/users\/58"}],"replies":[{"embeddable":true,"href":"https:\/\/www.careerlink.vn\/cam-nang-viec-lam\/wp-json\/wp\/v2\/comments?post=19136"}],"version-history":[{"count":9,"href":"https:\/\/www.careerlink.vn\/cam-nang-viec-lam\/wp-json\/wp\/v2\/posts\/19136\/revisions"}],"predecessor-version":[{"id":26109,"href":"https:\/\/www.careerlink.vn\/cam-nang-viec-lam\/wp-json\/wp\/v2\/posts\/19136\/revisions\/26109"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.careerlink.vn\/cam-nang-viec-lam\/wp-json\/wp\/v2\/media\/19188"}],"wp:attachment":[{"href":"https:\/\/www.careerlink.vn\/cam-nang-viec-lam\/wp-json\/wp\/v2\/media?parent=19136"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.careerlink.vn\/cam-nang-viec-lam\/wp-json\/wp\/v2\/categories?post=19136"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.careerlink.vn\/cam-nang-viec-lam\/wp-json\/wp\/v2\/tags?post=19136"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}