LinkedIn Data Scientist Interview Guide

One pattern keeps showing up with LinkedIn DS candidates: they prep for either a stats-heavy loop or an ML-heavy loop, not both. LinkedIn weights them almost equally, and the interview has a standalone Statistics & Probability round that most big tech companies have folded into other stages. Underestimate either pillar and you'll hit a wall.

LinkedIn Data Scientist Role

You're embedded with product and engineering on a specific surface (feed ranking, job recommendations, ads targeting) and own the full experiment lifecycle for that area. Success after year one means you've shipped experiments that led to real product decisions, not just analyses that sat in a slide deck. LinkedIn's internal experimentation platform, XLNT, is where you'll live, pulling session-level engagement metrics and presenting clear ship/no-ship recommendations to leadership.

A Typical Week

The writing allocation is the number that catches people off guard. You'll draft experiment design docs before tests launch, write up findings for LinkedIn's internal knowledge repo after, and close the week by scoping next week's hypotheses. This isn't busywork. Those docs are how decisions get made across pods. The other quiet surprise: you're expected to debug broken Spark jobs and trace data lineage in DataHub yourself when upstream schemas change, not file a ticket and wait.

Projects & Impact Areas

Feed ranking is where many DS roles sit, with teams iterating on content-quality signals and engagement models that directly affect ad monetization. Job recommendations under Talent Solutions present a different flavor of challenge, especially around cold-start problems for new members and the two-sided dynamics between recruiters and job seekers. LinkedIn's GenAI surface area (rated medium-weight in current skill expectations) is expanding, with DS roles increasingly focused on measuring and evaluating generative outputs rather than building the models themselves.

Skills & What's Expected

Both statistics and ML are scored at expert level, which is the unusual part. Most big tech DS roles skew toward one. The underrated dimension? Software engineering, scored high. You're writing production Python, doing code reviews on teammates' PRs, and owning data architecture decisions using Spark, Hadoop, and SQL. Candidates from research-heavy backgrounds who treat engineering as someone else's problem consistently wash out. Business acumen (also high) means framing every analysis around member growth or engagement impact, not model accuracy in isolation.

Levels & Career Growth

Most external hires land at Mid or Senior. The Senior-to-Staff jump is where careers stall, and it's not about building better models. It requires owning a problem space end-to-end (like feed quality measurement or recruiter matching evaluation) and mentoring other DSs across teams. LinkedIn's leveling maps to Microsoft's broader system since the 2016 acquisition, so verify level alignment before comparing raw TC numbers against offers from other companies.

Work Culture

The hybrid policy requires Tuesday through Thursday on the Sunnyvale campus, with Monday and Friday as remote days where deep focus work actually happens. From what candidates report, most people log off by 6 PM, and the culture genuinely discourages after-hours work. The "Act Like an Owner" value is real in practice: DSs are expected to proactively identify problems and propose solutions through the bi-weekly cross-org DS guild and direct product partnerships, not wait for a PM to assign a Jira ticket.

LinkedIn Data Scientist Compensation

LinkedIn RSUs vest at 25% per year on a straightforward annual schedule. Annual refresh grants are common and stack on top of your original vest, so the equity slice of your TC can grow meaningfully over time without any change in level. When evaluating an offer, factor in that LinkedIn's four business lines (Talent Solutions, Marketing Solutions, Premium Subscriptions, Learning) each have different growth trajectories, which affects how you think about the long-term value of those RSUs.

Base salary and the initial RSU grant are often the most negotiable components. Come prepared with specific numbers tied to your experience and the LinkedIn product area you'd be joining, whether that's feed ranking, job recommendations, or the newer GenAI evaluation work. Level alignment matters too: confirm with your recruiter exactly which LinkedIn level you're being considered for before comparing TC across companies, since titles alone can be misleading.

LinkedIn Data Scientist Interview Process

Seven rounds across roughly five weeks is a lot of context-switching. The biggest trap, from what candidates report, is over-indexing on coding prep while treating the Statistics & Probability video round as a warmup. It's not. That round covers hypothesis testing, A/B test design, sample size calculation, and conditional probability, and weak stats fundamentals are the single most cited rejection reason in LinkedIn's DS loop.

Your interviewers assess more than correctness. LinkedIn's process explicitly rewards clear communication and structured thinking, so a correct answer you can't explain well lands worse than you'd expect. During each round, articulate your assumptions out loud, name the tradeoffs in your metric choices, and connect your analysis back to a real LinkedIn product like Talent Solutions or feed ranking. Giving your interviewer something concrete to reference in their writeup matters more than speed.

LinkedIn Data Scientist Interview Questions

/*
Daily Active Searchers (28-day rolling) for last 60 days.
Assumptions:
- search_events.event_ts is a timestamp in UTC.
- Multiple events per member per day should count once.
- Output includes all days in the last 60 days, even if zero.
Dialect: ANSI-ish. If your warehouse supports GENERATE_SERIES, use that.
*/

WITH params AS (
  SELECT
    CAST(CURRENT_DATE AS DATE) AS as_of_date,
    CAST(CURRENT_DATE - INTERVAL '59' DAY AS DATE) AS start_date
),

-- Date spine for last 60 days (inclusive)
date_spine AS (
  SELECT p.start_date AS dt
  FROM params p
  UNION ALL
  SELECT dt + INTERVAL '1' DAY
  FROM date_spine
  JOIN params p ON 1 = 1
  WHERE dt < p.as_of_date
),

-- One row per member per active day
member_search_day AS (
  SELECT
    se.member_id,
    CAST(se.event_ts AS DATE) AS search_date
  FROM search_events se
  JOIN params p
    ON CAST(se.event_ts AS DATE) BETWEEN (p.start_date - INTERVAL '27' DAY) AND p.as_of_date
  GROUP BY
    se.member_id,
    CAST(se.event_ts AS DATE)
)

SELECT
  ds.dt AS event_date,
  COUNT(DISTINCT msd.member_id) AS active_searchers_28d
FROM date_spine ds
LEFT JOIN member_search_day msd
  ON msd.search_date BETWEEN (ds.dt - INTERVAL '27' DAY) AND ds.dt
GROUP BY ds.dt
ORDER BY ds.dt

-- Needed for recursive CTE in many warehouses
OPTION (MAXRECURSION 1000);

from __future__ import annotations

from collections import defaultdict
from dataclasses import dataclass
from typing import Dict, Iterable, List, Tuple, Any


@dataclass
class AuthorStats:
    impressions: int = 0
    clicks: int = 0

    @property
    def ctr(self) -> float:
        return self.clicks / self.impressions if self.impressions > 0 else 0.0


def top_k_authors_by_ctr(
    events: Iterable[Dict[str, Any]],
    k: int,
    min_impressions: int = 1,
) -> List[Dict[str, Any]]:
    """Return top-k authors by CTR from an event stream.

    Each event is expected to have keys: author_id, action.
    action must be one of {'impression', 'click'}. Unknown actions are ignored.

    Tie-breakers: higher CTR, then higher impressions, then smaller author_id.
    """
    if k <= 0:
        return []

    stats: Dict[int, AuthorStats] = defaultdict(AuthorStats)

    for e in events:
        if not isinstance(e, dict):
            continue
        if "author_id" not in e or "action" not in e:
            continue

        try:
            author_id = int(e["author_id"])
        except (TypeError, ValueError):
            continue

        action = e["action"]
        if action == "impression":
            stats[author_id].impressions += 1
        elif action == "click":
            stats[author_id].clicks += 1
        else:
            continue

    eligible: List[Tuple[float, int, int]] = []
    for author_id, s in stats.items():
        if s.impressions >= min_impressions:
            eligible.append((s.ctr, s.impressions, author_id))

    eligible.sort(key=lambda t: (-t[0], -t[1], t[2]))

    out: List[Dict[str, Any]] = []
    for ctr, impressions, author_id in eligible[:k]:
        out.append(
            {
                "author_id": author_id,
                "ctr": ctr,
                "impressions": impressions,
                "clicks": stats[author_id].clicks,
            }
        )
    return out


if __name__ == "__main__":
    sample = [
        {"user_id": 1, "author_id": 10, "ts": 1, "action": "impression"},
        {"user_id": 1, "author_id": 10, "ts": 2, "action": "click"},
        {"user_id": 2, "author_id": 10, "ts": 3, "action": "impression"},
        {"user_id": 3, "author_id": 11, "ts": 4, "action": "impression"},
        {"user_id": 3, "author_id": 11, "ts": 5, "action": "click"},
        {"user_id": 4, "author_id": 11, "ts": 6, "action": "click"},
        {"user_id": 5, "author_id": 12, "ts": 7, "action": "impression"},
    ]
    print(top_k_authors_by_ctr(sample, k=3, min_impressions=1))

from __future__ import annotations

from typing import Iterable, List, Tuple, Optional


def max_events_in_rolling_window(
    timestamps: Iterable[int],
    W: int,
) -> Tuple[int, Optional[Tuple[int, int]]]:
    """Return (max_count, (start, end)) for a rolling window of length W.

    Window semantics: [start, end) with end = start + W.
    An event at time t is included if start <= t < end.

    If timestamps is empty, returns (0, None).
    """
    ts: List[int] = sorted(int(t) for t in timestamps)
    if W <= 0:
        raise ValueError("W must be positive")
    if not ts:
        return 0, None

    i = 0
    best_count = 0
    best_window: Tuple[int, int] = (ts[0], ts[0] + W)

    for j in range(len(ts)):
        # Maintain half-open window length W: ts[j] must satisfy ts[j] < ts[i] + W
        while i <= j and ts[j] >= ts[i] + W:
            i += 1

        count = j - i + 1
        if count > best_count:
            best_count = count
            start = ts[i]
            best_window = (start, start + W)

    return best_count, best_window


if __name__ == "__main__":
    sample = [10, 10, 15, 20, 21, 29, 30, 30, 39]
    print(max_events_in_rolling_window(sample, W=10))

The top two slices both test your ability to reason under uncertainty, but they do it from opposite directions: one asks you to design and validate experiments, the other asks you to build and critique models. When a Product Sense question hands you conflicting metrics on a feed ranking change (sessions up, hides up), you can't answer it well without the statistical intuition to question whether the lift is real and the ML instinct to ask what the ranking objective actually optimized. From what candidates report, the most common prep gap is treating statistics as a refresher topic rather than a primary study area, even though it carries more weight than any other single category.

Practice LinkedIn-specific questions across all seven areas at datainterview.com/questions.

How to Prepare for LinkedIn Data Scientist Interviews

The GenAI push is where the DS org is moving fastest. LinkedIn's engineering team published a detailed breakdown of their GenAI application tech stack and followed it with a piece on extending that stack to support AI agents. Read both before your loop. They reveal specific architectural choices (retrieval-augmented generation pipelines, guardrail layers, evaluation frameworks) that give you concrete material for product sense and ML design answers.

Most candidates fumble "why LinkedIn" by staying abstract. What actually impresses: describe a tension unique to LinkedIn's two-sided marketplace. For example, job recommendations have to satisfy both seekers and recruiters, and optimizing click-through for one side can tank match quality for the other. Or talk about how network interference makes A/B testing on a professional graph with 1B+ members structurally harder than on a standard consumer feed. These are problems you can't copy-paste from a Meta or Google prep script.

Try a Real Interview Question

| ab_assignments |
|----------------|
| user_id | experiment_id | variant   | assigned_at |
|---------|---------------|-----------|-------------|
| 101     | notif_rank_v1 | control   | 2026-01-01  |
| 102     | notif_rank_v1 | treatment | 2026-01-01  |
| 103     | notif_rank_v1 | control   | 2026-01-02  |
| 104     | notif_rank_v1 | treatment | 2026-01-02  |

| user_segments |
|---------------|
| user_id | segment |
|---------|---------|
| 101     | premium |
| 102     | premium |
| 103     | free    |
| 104     | free    |

| notif_events |
|--------------|
| user_id | event_date  | event_type |
|---------|-------------|------------|
| 101     | 2026-01-05  | impression |
| 101     | 2026-01-05  | click      |
| 102     | 2026-01-05  | impression |
| 103     | 2026-01-05  | impression |
| 104     | 2026-01-05  | impression |

WITH params AS (
  SELECT
    'notif_rank_v1' AS experiment_id,
    DATE '2026-01-01' AS start_date,
    DATE '2026-01-31' AS end_date
),
assigned_users AS (
  SELECT a.user_id, a.variant
  FROM ab_assignments a
  JOIN params p
    ON a.experiment_id = p.experiment_id
),
events_agg AS (
  SELECT
    e.user_id,
    SUM(CASE WHEN e.event_type = 'impression' THEN 1 ELSE 0 END) AS impressions,
    SUM(CASE WHEN e.event_type = 'click' THEN 1 ELSE 0 END) AS clicks
  FROM notif_events e
  JOIN params p
    ON e.event_date BETWEEN p.start_date AND p.end_date
  GROUP BY e.user_id
),
user_level AS (
  SELECT
    au.variant,
    us.segment,
    ea.impressions,
    ea.clicks
  FROM assigned_users au
  JOIN user_segments us
    ON us.user_id = au.user_id
  JOIN events_agg ea
    ON ea.user_id = au.user_id
  WHERE ea.impressions >= 1
),
segment_variant AS (
  SELECT
    segment,
    variant,
    SUM(clicks) AS clicks,
    SUM(impressions) AS impressions,
    CAST(SUM(clicks) AS DOUBLE PRECISION) / NULLIF(SUM(impressions), 0) AS ctr
  FROM user_level
  GROUP BY segment, variant
)
SELECT
  s.segment,
  MAX(CASE WHEN s.variant = 'control' THEN s.ctr END) AS ctr_control,
  MAX(CASE WHEN s.variant = 'treatment' THEN s.ctr END) AS ctr_treatment,
  COALESCE(MAX(CASE WHEN s.variant = 'treatment' THEN s.ctr END), 0)
  - COALESCE(MAX(CASE WHEN s.variant = 'control' THEN s.ctr END), 0) AS delta
FROM segment_variant s
GROUP BY s.segment
ORDER BY s.segment;

LinkedIn's coding round leans on scenarios tied to their professional graph: think multi-hop connection queries, endorsement aggregation, or engagement decay across content types. The problems reward candidates who can model relational data cleanly in Python, not just pass algorithmic edge cases. Build that muscle at datainterview.com/coding.

Test Your Readiness

Stats and ML together make up over 40% of the question distribution, so prioritize those categories first at datainterview.com/questions.